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How to Write and Publish a Research Paper for a Peer-Reviewed Journal

Clara busse.

1 Department of Maternal and Child Health, University of North Carolina Gillings School of Global Public Health, 135 Dauer Dr, 27599 Chapel Hill, NC USA

Ella August

2 Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029 USA

Associated Data

Communicating research findings is an essential step in the research process. Often, peer-reviewed journals are the forum for such communication, yet many researchers are never taught how to write a publishable scientific paper. In this article, we explain the basic structure of a scientific paper and describe the information that should be included in each section. We also identify common pitfalls for each section and recommend strategies to avoid them. Further, we give advice about target journal selection and authorship. In the online resource 1 , we provide an example of a high-quality scientific paper, with annotations identifying the elements we describe in this article.

Electronic supplementary material

The online version of this article (10.1007/s13187-020-01751-z) contains supplementary material, which is available to authorized users.

Introduction

Writing a scientific paper is an important component of the research process, yet researchers often receive little formal training in scientific writing. This is especially true in low-resource settings. In this article, we explain why choosing a target journal is important, give advice about authorship, provide a basic structure for writing each section of a scientific paper, and describe common pitfalls and recommendations for each section. In the online resource 1 , we also include an annotated journal article that identifies the key elements and writing approaches that we detail here. Before you begin your research, make sure you have ethical clearance from all relevant ethical review boards.

Select a Target Journal Early in the Writing Process

We recommend that you select a “target journal” early in the writing process; a “target journal” is the journal to which you plan to submit your paper. Each journal has a set of core readers and you should tailor your writing to this readership. For example, if you plan to submit a manuscript about vaping during pregnancy to a pregnancy-focused journal, you will need to explain what vaping is because readers of this journal may not have a background in this topic. However, if you were to submit that same article to a tobacco journal, you would not need to provide as much background information about vaping.

Information about a journal’s core readership can be found on its website, usually in a section called “About this journal” or something similar. For example, the Journal of Cancer Education presents such information on the “Aims and Scope” page of its website, which can be found here: https://www.springer.com/journal/13187/aims-and-scope .

Peer reviewer guidelines from your target journal are an additional resource that can help you tailor your writing to the journal and provide additional advice about crafting an effective article [ 1 ]. These are not always available, but it is worth a quick web search to find out.

Identify Author Roles Early in the Process

Early in the writing process, identify authors, determine the order of authors, and discuss the responsibilities of each author. Standard author responsibilities have been identified by The International Committee of Medical Journal Editors (ICMJE) [ 2 ]. To set clear expectations about each team member’s responsibilities and prevent errors in communication, we also suggest outlining more detailed roles, such as who will draft each section of the manuscript, write the abstract, submit the paper electronically, serve as corresponding author, and write the cover letter. It is best to formalize this agreement in writing after discussing it, circulating the document to the author team for approval. We suggest creating a title page on which all authors are listed in the agreed-upon order. It may be necessary to adjust authorship roles and order during the development of the paper. If a new author order is agreed upon, be sure to update the title page in the manuscript draft.

In the case where multiple papers will result from a single study, authors should discuss who will author each paper. Additionally, authors should agree on a deadline for each paper and the lead author should take responsibility for producing an initial draft by this deadline.

Structure of the Introduction Section

The introduction section should be approximately three to five paragraphs in length. Look at examples from your target journal to decide the appropriate length. This section should include the elements shown in Fig.  1 . Begin with a general context, narrowing to the specific focus of the paper. Include five main elements: why your research is important, what is already known about the topic, the “gap” or what is not yet known about the topic, why it is important to learn the new information that your research adds, and the specific research aim(s) that your paper addresses. Your research aim should address the gap you identified. Be sure to add enough background information to enable readers to understand your study. Table ​ Table1 1 provides common introduction section pitfalls and recommendations for addressing them.

The main elements of the introduction section of an original research article. Often, the elements overlap

Common introduction section pitfalls and recommendations

Methods Section

The purpose of the methods section is twofold: to explain how the study was done in enough detail to enable its replication and to provide enough contextual detail to enable readers to understand and interpret the results. In general, the essential elements of a methods section are the following: a description of the setting and participants, the study design and timing, the recruitment and sampling, the data collection process, the dataset, the dependent and independent variables, the covariates, the analytic approach for each research objective, and the ethical approval. The hallmark of an exemplary methods section is the justification of why each method was used. Table ​ Table2 2 provides common methods section pitfalls and recommendations for addressing them.

Common methods section pitfalls and recommendations

Results Section

The focus of the results section should be associations, or lack thereof, rather than statistical tests. Two considerations should guide your writing here. First, the results should present answers to each part of the research aim. Second, return to the methods section to ensure that the analysis and variables for each result have been explained.

Begin the results section by describing the number of participants in the final sample and details such as the number who were approached to participate, the proportion who were eligible and who enrolled, and the number of participants who dropped out. The next part of the results should describe the participant characteristics. After that, you may organize your results by the aim or by putting the most exciting results first. Do not forget to report your non-significant associations. These are still findings.

Tables and figures capture the reader’s attention and efficiently communicate your main findings [ 3 ]. Each table and figure should have a clear message and should complement, rather than repeat, the text. Tables and figures should communicate all salient details necessary for a reader to understand the findings without consulting the text. Include information on comparisons and tests, as well as information about the sample and timing of the study in the title, legend, or in a footnote. Note that figures are often more visually interesting than tables, so if it is feasible to make a figure, make a figure. To avoid confusing the reader, either avoid abbreviations in tables and figures, or define them in a footnote. Note that there should not be citations in the results section and you should not interpret results here. Table ​ Table3 3 provides common results section pitfalls and recommendations for addressing them.

Common results section pitfalls and recommendations

Discussion Section

Opposite the introduction section, the discussion should take the form of a right-side-up triangle beginning with interpretation of your results and moving to general implications (Fig.  2 ). This section typically begins with a restatement of the main findings, which can usually be accomplished with a few carefully-crafted sentences.

Major elements of the discussion section of an original research article. Often, the elements overlap

Next, interpret the meaning or explain the significance of your results, lifting the reader’s gaze from the study’s specific findings to more general applications. Then, compare these study findings with other research. Are these findings in agreement or disagreement with those from other studies? Does this study impart additional nuance to well-accepted theories? Situate your findings within the broader context of scientific literature, then explain the pathways or mechanisms that might give rise to, or explain, the results.

Journals vary in their approach to strengths and limitations sections: some are embedded paragraphs within the discussion section, while some mandate separate section headings. Keep in mind that every study has strengths and limitations. Candidly reporting yours helps readers to correctly interpret your research findings.

The next element of the discussion is a summary of the potential impacts and applications of the research. Should these results be used to optimally design an intervention? Does the work have implications for clinical protocols or public policy? These considerations will help the reader to further grasp the possible impacts of the presented work.

Finally, the discussion should conclude with specific suggestions for future work. Here, you have an opportunity to illuminate specific gaps in the literature that compel further study. Avoid the phrase “future research is necessary” because the recommendation is too general to be helpful to readers. Instead, provide substantive and specific recommendations for future studies. Table ​ Table4 4 provides common discussion section pitfalls and recommendations for addressing them.

Common discussion section pitfalls and recommendations

Follow the Journal’s Author Guidelines

After you select a target journal, identify the journal’s author guidelines to guide the formatting of your manuscript and references. Author guidelines will often (but not always) include instructions for titles, cover letters, and other components of a manuscript submission. Read the guidelines carefully. If you do not follow the guidelines, your article will be sent back to you.

Finally, do not submit your paper to more than one journal at a time. Even if this is not explicitly stated in the author guidelines of your target journal, it is considered inappropriate and unprofessional.

Your title should invite readers to continue reading beyond the first page [ 4 , 5 ]. It should be informative and interesting. Consider describing the independent and dependent variables, the population and setting, the study design, the timing, and even the main result in your title. Because the focus of the paper can change as you write and revise, we recommend you wait until you have finished writing your paper before composing the title.

Be sure that the title is useful for potential readers searching for your topic. The keywords you select should complement those in your title to maximize the likelihood that a researcher will find your paper through a database search. Avoid using abbreviations in your title unless they are very well known, such as SNP, because it is more likely that someone will use a complete word rather than an abbreviation as a search term to help readers find your paper.

After you have written a complete draft, use the checklist (Fig. ​ (Fig.3) 3 ) below to guide your revisions and editing. Additional resources are available on writing the abstract and citing references [ 5 ]. When you feel that your work is ready, ask a trusted colleague or two to read the work and provide informal feedback. The box below provides a checklist that summarizes the key points offered in this article.

Checklist for manuscript quality

(PDF 362 kb)

Acknowledgments

Ella August is grateful to the Sustainable Sciences Institute for mentoring her in training researchers on writing and publishing their research.

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The authors declare that they have no conflict of interest.

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APA: Citing Journal Articles  from  Lawrence W. Tyree Library  on  Vimeo . View a transcript  here.

In this tutorial, you will learn the basics for citing journal articles with and without a DOI and how to cite open access journal articles.

Every APA reference needs four parts:  author, date, title,  and  source . As you go through these examples, you will learn how to identify these four parts and how to place and format them into a proper APA reference.

Example 1: A Journal Article with a DOI

For the first example, you will learn how to cite a journal article with a DOI. Often, you will find journal articles online using the library's databases or other online resources. 

The first step is to identify the  author  of the article. The author of this article is Brittanie Atteberry-Ash,

To list an author, write the  last name , a  comma , and the  first and middle initials .

Example: Atteberry-Ash.

Next, identify when this article was published. For journal articles, you typically only need the  year . In this case, this article was published in 2022. You can usually find the date at the top of the article, the cover of the journal, or, for online articles, the article's record.

List the  date  after the author(s), in  parentheses , followed by a  period .

Example: Atteberry-Ash, B. (2022).

Now, identify the  title of the article . The title will usually be at the very top of the article, in a larger size font.

List the  title  of the article after the date. Make sure you only capitalize the  first word of the title ,  the first word of the subtitle , which comes after a colon, and any  proper nouns . End with a period. In this title, only the words Social  and  A  are capitalized.

Example: Atteberry-Ash, B. (2022). Social work and social justice: A conceptual review.

For the last component, you need the  source . For an article, this is the  title of the journal, volume, issue , which is sometimes called  number , and  page numbers  of the article. Usually this information can be found on the cover of the journal, on the table of contents, or at the top of the article. For the page numbers, you should look at the first and last pages of the article. For online articles, this information is usually found in the article's record.

Type the  journal title , in  italics , capitalizing all major words, a comma, the  volume , also in  italics , the  number or issue  in parentheses, a comma, and then the  page numbers  of the article.

Example:  Atteberry-Ash, B. (2022). Social work and social justice: A conceptual review.  Social Work,   68  (1), 38-46.

The last element of the  source  is the  DOI , which stands for Digital Object Identifier. A DOI can be found in the article’s record or on the first page of the article.

Type the  DOI , using the prefix  https://doi.org/ . There is no period after the DOI.

Example:  Atteberry-Ash, B. (2022). Social work and social justice: A conceptual review.  Social Work,   68  (1), 38-46. https://doi.org/10.1093/sw/swac042

If you refer to a work in your paper, either by directly quoting, paraphrasing, or by referring to main ideas, you will need to include an in-text parenthetical citation. There are a number of ways to do this. In this example, a  signal phrase  is used to introduce a direct quote. The  author's name  is given in the text, and the  publication date  and  page number(s)  are enclosed in parentheses at the beginning and end of the sentence.

Example: Atteberry-Ash (2022) notes "social workers are called on to practice socially just values and to address the consequences of oppression, specifically lost opportunity, social disenfranchisement, and isolation" (p. 38).

Example 2: Multiple Authors and No DOI

In this example, most of the components needed for the reference can be found in the article’s record. This article, however, has multiple authors and does not have a DOI listed in its record or in the article itself.

Format all the citation components of this journal article like the first example. For multiple authors, list the authors in the order they are listed in the article. Use a  comma  to separate each author and an  ampersand (&)  should be placed before the last author’s name. This applies for articles with up to twenty authors. Since there is no DOI listed for this article, simply omit that element. The reference will conclude after the page numbers.  

Example: Penprase, B., Mileto, L., Bittinger, A., Hranchook, A. M., Atchley, J. A., Bergakker, S., Eimers, T., & Franson, H. (2012). The use of high-fidelity simulation in the admissions process: One nurse anesthesia program’s experience.  AANA Journal, 80 (1), 43–48.

If you refer to a work in your paper that has three or more authors, the in-text citation will include the first author's name only, followed by  et al.  which means "and all the rest."

Example: Penprase et al. (2012) states that "Admission into nurse anesthesia programs is known to be a competitive process among a diverse pool of candidates" (p. 43).

Example 3: An Open Access Journal Article

This article was found in  PLOS One  which is an open access journal. Open access journal articles are articles with the full text freely available online and do not require logging in.

You will need all of the same information from the previous examples to cite an open access article. In this example, most of this information can be found at the top of the article.

In this example, the article's volume, issue, and the  article number  are found in the citation provided by the journal. Article numbers are used in place of page numbers in some online journals.

The format for open access journals is the same as the other examples. In this example, an article number is used in place of the page numbers. After the issue number, type  Article  and then the article number. If an open access journal does not provide a DOI, you may provide the URL of the article instead. Only include the URL if it directly brings you to the full text of the article without logging in.

Example: Francis, H. M., Stevenson, R. J., Chambers, J. R., Gupta, D., Newey, B., & Lim, C. K. (2019). A brief diet intervention can reduce symptoms of depression in young adults – A randomised controlled trial.  PLOS ONE, 14 (1), Article e0222768. https://doi.org/10.1371/journal.pone.0222768

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Resolving a Candidate Dual Active Galactic Nucleus with ∼100 pc Separation in MCG-03-34-64

Anna Trindade Falcão 1 , T. J. Turner 2 , S. B. Kraemer 3 , J. Reeves 3,4 , V. Braito 3,4,5 , H. R. Schmitt 6 , and L. Feuillet 3

Published 2024 September 9 • © 2024. The Author(s). Published by the American Astronomical Society. The Astrophysical Journal , Volume 972 , Number 2 Citation Anna Trindade Falcão et al 2024 ApJ 972 185 DOI 10.3847/1538-4357/ad6b91

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Author affiliations.

1 Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138, USA

2 Eureka Scientific, Inc., 2452 Delmer St., Suite 100, Oakland, CA 94602, USA

3 Institute for Astrophysics and Computational Sciences, The Catholic University of America, Washington, DC 20064, USA

4 INAF—Osservatorio Astronomico di Brera, Via Bianchi 46, 23807, Merate (LC), Italy

5 Dipartimento di Fisica, Università di Trento, Via Sommarive 14, Trento 38123, Italy

6 Naval Research Laboratory, Washington, DC 20375, USA

Anna Trindade Falcão https://orcid.org/0000-0001-8112-3464

T. J. Turner https://orcid.org/0000-0003-2971-1722

S. B. Kraemer https://orcid.org/0000-0003-4073-8977

J. Reeves https://orcid.org/0000-0003-3221-6765

V. Braito https://orcid.org/0000-0002-2629-4989

H. R. Schmitt https://orcid.org/0000-0003-2450-3246

L. Feuillet https://orcid.org/0000-0002-5718-2402

• Received 2024 May 24
• Revised 2024 July 20
• Accepted 2024 August 4
• Published 2024 September 9

AGN host galaxies ; Seyfert galaxies ; High energy astrophysics

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We report the serendipitous multiwavelength discovery of a candidate dual black hole system with a separation of ∼100 pc, in the gas-rich luminous infrared galaxy MCG-03-34-64 ( z = 0.016). Hubble Space Telescope/Advanced Camera for Surveys observations show three distinct optical centroids in the [O iii ] narrow-band and F814W images. Subsequent analysis of Chandra/ACIS data shows two spatially resolved peaks of equal intensity in the neutral Fe K α (6.2–6.6 keV) band, while high-resolution radio continuum observations with the Very Large Array at 8.46 GHz (3.6 cm band) show two spatially coincident radio peaks. Fast shocks as the ionizing source seem unlikely, given the energies required for the production of Fe K α . If confirmed, the separation of ∼100 pc would represent the closest dual active galactic nuclei reported to date with spatially resolved, multiwavelength observations.

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1. Introduction

The masses of supermassive black holes (SMBHs) in active galactic nuclei (AGNs) correlate with the global properties of their host galaxies' stellar components, such as luminosity, mass, and velocity dispersion, extending over kiloparsec scales (e.g., Kormendy & Ho 2013 ). This correlation highlights the need to understand the mechanisms driving SMBH growth.

Both galactic evolutionary models and observations suggest that a significant fraction of AGNs, particularly those at the center of large-scale structures, undergo major mergers (e.g., De Lucia & Blaizot 2007 ; Hopkins et al. 2008 ; Ginolfi et al. 2017 ; Castignani et al. 2020 ). Hydrodynamical simulations further demonstrate that major mergers induce gas inflows toward galactic centers, potentially triggering both star formation and accretion onto central SMBHs (Mayer et al. 2007 ). However, the overall impact of these events on SMBH growth throughout cosmic time remains poorly constrained.

SMBH pairs, often manifested as dual AGNs, provide distinctive evidence for merger-fueled SMBH growth (e.g., Wassenhove et al. 2012 ). Numerous dual AGN candidates have been identified using various techniques, including optical spectroscopy with emission line ratios (e.g., Liu et al. 2011 ), hard X-ray emission (e.g., Koss et al. 2011 ), and double-peaked narrow emission lines (e.g., Smith et al. 2010 ; Koss et al. 2023 ). Nonetheless, these methods have limitations, and multiwavelength follow-up observations have revealed a substantial number of false positives (e.g., Fu et al. 2011b ).

The advent of gravitational-wave astronomy, with the potential for detection through pulsar timing arrays (e.g., Verbiest et al. 2016 ), has heightened the importance of understanding the formation timescales of binary systems. Studying kiloparsec and subkiloparsec dual AGNs offers a unique window into the final stages of SMBH binary coalescence, a crucial process in gravitational wave astronomy.

Dual AGNs separated by kiloparsec or subkiloparsec scales are inherently more challenging to detect and investigate than wider-separation systems (e.g., >3 kpc). This difficulty arises from increased obscuration in late-stage mergers (e.g., Koss et al. 2016 ; Ricci et al. 2021 ; De Rosa et al. 2022 ), limitations in telescope spatial resolution (particularly at subkiloparsec scales), the scarcity of detected radio-bright dual systems (Burke-Spolaor 2011 ), and the limitations of optical selection using double-peaked narrow emission lines (prone to false positives; see Fu et al. 2011a ). Existing observations of dual AGNs tentatively suggest that AGN triggering becomes more prevalent in advanced mergers with stellar bulge separations <10 kpc (e.g., Koss et al. 2010 ; Fu et al. 2018 ; Stemo et al. 2021 ), aligning with simulations of SMBH accretion and evolution in such mergers (e.g., Blecha et al. 2018 ). Therefore, studying nearby galaxies hosting dual AGNs separated at subkiloparsec scales is crucial for advancing our understanding of the late stages of galaxy mergers, the triggering and fueling of AGN activity, and the dynamics of SMBH pairs (Steinborn et al. 2016 ). These close-separation systems provide a unique window into the processes leading to the eventual coalescence of SMBHs, which is a major source of gravitational waves, and plays a fundamental role in the growth of SMBHs and their host galaxies (Dotti et al. 2012 ; Kharb et al. 2017 ).

While several dual AGN candidates have been proposed at scales of hundreds of parsecs, often supported by single-wave band observations, these have frequently been challenged by subsequent studies. Notable examples include the nearby Seyfert NGC 3393 (Fabbiano et al. 2011 ), SDSS J101022.95 + 141300.9 (Goulding et al. 2019 ), and a third active nucleus in NGC 6240 (Kollatschny et al. 2020 ), later disputed in other works (Koss et al. 2015 ; Veres et al. 2021 ; Treister et al. 2020 ).

In this study, we present the serendipitous discovery of a candidate dual AGN system in MCG-03-34-64 (IRAS 13197-1627), a nearby early-type infrared luminous galaxy at z = 0.01654 (∼78 Mpc, from NASA/IPAC Extragalactic Database). 7 This galaxy is identified as one of the hardest X-ray sources in the local Universe (Tatum et al. 2016 ). Earlier X-ray observations with ASCA, XMM-Newton, and BeppoSAX (Dadina & Cappi 2004 ; Miniutti et al. 2007 ) revealed an extremely hard and complex source spectrum, attributed to heavy absorption from a multilayered and clumpy medium. MCG-03-34-64 also shows extended radio emission (∼300 pc), roughly aligned with the major axis of the host galaxy (Schmitt et al. 2001 ), and ∼2'' extent in mid-infrared aligned in the same direction as the radio structure (Hönig et al. 2010 ).

We have obtained Hubble Space Telescope/Advanced Camera for Surveys (ACS) imaging of MCG-03-34-64 in 2022 June (P.I.: Turner, proposal ID: 16847), and 50 ks of Chandra/ACIS-S observations in 2023 April (obs ids 25253, 27802, and 27803, P.I.: Turner). This paper presents the results of the analysis of these new data sets, combined with existing Very Large Array (VLA) radio, and Hubble Space Telescope (HST) optical imaging of the source. Throughout this paper, we adopt Ω m = 0.3, Ω Λ = 0.7, and H 0 = 70 km s −1 Mpc −1 , and a scale of 340 pc arcsec −1 , based on the redshift at the galaxy's distance.

2. Multiwavelength Observations and Analysis

Table 1 lists all observations used in this paper, including instruments, filters, observation dates, obs ids, and exposure times. Details on the reduction of new HST/ACS and Chandra/ACIS-S observations are provided in Sections 2.1 and 2.2 , respectively. For reduction and analysis of archival HST F814W, VLA 8.46 GHz imaging, and Suzaku and XMM-Newton spectroscopy, see Section 2.3 . All the HST data used in this paper can be found in MAST at doi: 10.17909/53rj-fw34 .

Table 1.  Multiwavelength Observations of MCG-03-34-64

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2.1. Hubble Space Telescope Imaging

HST/ACS observations of MCG-03-34-64 were obtained using the linear ramp filter FR505N (narrow-band [O iii ]) centered at 5089.6 Å, to characterize the morphology of the emission-line gas, while a continuum medium band image was obtained using FR647M, centered at 5590 Å. These filters have bandwidths of 2% and 9%, respectively. Standard HST pipeline procedures were employed for data reduction. The narrow-band and continuum images were acquired sequentially and did not require realignment. Flux calibration was performed using information available on the headers.

2.2. Chandra Imaging and Spectroscopy.

Subpixel imaging binning was employed to effectively oversample the Chandra point-spread function (PSF) and overcome the limitations of the ACIS instrumental pixel size. This method has been extensively used and validated in previous studies examining the subkiloparsec regions around nearby and obscured AGNs (e.g., Maksym et al. 2017 ; Fabbiano et al. 2018a ; Ma et al. 2021 ; Trindade Falcão 2023 ), demonstrating excellent agreement between reconstructed ACIS-S features and those imaged with higher spatial resolution instruments such as HST and VLA (e.g., Wang et al. 2011b ; Paggi et al. 2012 ; Maksym et al. 2019 ; Fabbiano et al. 2018b ). The Chandra PSF was simulated using ChaRT 9 and MARX . 10 This work uses a final Chandra scale of one-eighth of the native ACIS pixel.

2.3. Archival Radio/Optical/X-Ray Observations

In addition to the new Chandra and HST data sets, we analyze archival optical, radio, and X-ray observations of MCG-03-34-64, as listed in Table 1 . These data include 8.46 GHz radio imaging with VLA, optical continuum imaging with HST/ACS F814W, and X-ray spectra from Suzaku and XMM-Newton.

There are four additional archival Chandra observations with MCG-03-34-64 in the field of view (obs ids 27267, 27786, 7373, and 23690). However, three of these observations are not usable due to the galaxy being located at the very edge of the field (observations were optimized for the companion galaxy). The fourth available Chandra observation consists of a 7 ks snapshot (used in Miniutti et al. 2007 ), which has insufficient counts for meaningful imaging analysis.

3.1. Imaging Analysis

3.1.1. hubble space telescope imaging.

The [O iii ] narrow-line region (NLR) in MCG-03-34-64 has a highly unusual morphology, featuring three distinct, and compact emission regions, as shown in Figure 1 . The NLR extends ∼2.3 kpc along the NE–SW direction. In the perpendicular direction (NW–SE), we observe three diffraction spikes characteristic of point sources, while one diffraction spike is observed along the NE–SW cone. These features suggest high concentrations of [O iii ] gas within a relatively small region, a rare occurrence in the local Universe (Fischer et al. 2018 ).

Figure 1.  HST [O iii ] and F814W images of the central region of MCG-03-34-64. Note the prominent diffraction spikes present in the [O iii ] image.

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To examine the overall structure of the NLR, we model the [O iii ] light distribution with GALFIT (Peng et al. 2002 , 2010 ). The modeling was performed with generic Sérsic profiles, and we allow all parameters to freely vary during the fitting process. The background level and standard deviation were determined from blank regions within the image's field of view.

The best-fit GALFIT model consists of four Sérsic components, one for each of the three peaks visible in the [O iii ] surface plot in the leftmost panel of Figure 2 , and an additional larger component to take into account the underlying fainter, more extended emission. In the second panel, we show a zoomed-in image of the [O iii ] emission, and mark the position of each Sérsic component corresponding to a strong peak of [O iii ] emission as green circles. The third panel of Figure 2 shows the best-fit GALFIT model, while the fourth panel shows the residual images from this best-fit model, with the different model components subtracted from the data.

Figure 2.  First panel: surface plot of the [O iii ] flux in the HST/ACS image where three distinct sources are visible. Second panel: HST/ACS [O iii ] image showing the presence of three closely separated emission peaks. We mark the position of each Sérsic component for the emission peaks as green circles. Third panel: best-fit GALFIT model, using four Sérsic components. Fourth panel: residual image after subtraction of the best-fit GALFIT model.

Table 2 lists the best-fit model parameters. The strong [O iii ] peaks of emission are fit with Sérsic components with indices 0.41 ≤ n ≤ 0.51, indicating that they are similar to Gaussians ( n = 0.5), but in two cases show a slightly more centrally concentrated distribution. Given that these components have effective radii ∼2–3 times that of an unresolved source, we opted to not include a PSF component in the fitting model, since it is not expected to significantly change the results.

Given the unique morphology of the optical emission observed in MCG-03-34-64 with HST, we examine other available multiwavelength observations (Table 1 ) to obtain a more comprehensive picture of this inner region.

3.1.2. Chandra Imaging

Figure 3 shows the inner ∼200 pc region of MCG-03-34-64, as observed with Chandra/ACIS in different energy bands. The images are binned at one-eighth of the native pixel to use the full-resolution of the instrument in the high-count inner region, and processed with 1 kernel Gaussian smoothing.

Figure 3.  Chandra/ACIS-S merged data set showing the inner 200 pc region of MCG-03-34-64 in different bands (one-eighth subpixel, and 1 kernel Gaussian smoothing). First panel: soft (0.3–3 keV) X-ray image. Second panel: 3–5 keV hard continuum image. Third panel: Fe K α (6.2–6.6 keV) Chandra image. We show the location of the two Fe K α centroids as blue circles. Fourth panel: 6–7 keV hard band Chandra image.

We measure nearly equal X-ray luminosities in the narrow 6.2–6.6 keV band from the Chandra image for both Fe K α peaks, L (6.2−6.6 keV) ∼ 3.2 ± 0.6 × 10 40 erg s −1 , for D ∼ 78 Mpc (e.g., de Grijp et al. 1992 ; Table 3 ).

Table 3.  Chandra/ACIS-S Merged Data Set—Astrometry, Fluxes, and Luminosities of the Individual Fe K α Regions

Note. The luminosities are calculated for D = 78 Mpc.

To address concerns that the dual morphology might be a spurious detection due to smoothing on scales smaller than the Chandra PSF, we examine the individual Chandra observations (prior to merging) listed in Table 1 , in the Fe K α band (Figure 4 ). These images are binned at one-eighth of the native pixel and smoothed with a 1 kernel Gaussian. As shown, the dual morphology of the Fe K α band is indeed observed in all individual observations, prior to merging, confirming the robustness of the detection. The differences in surface brightness between the individual Chandra exposures seen in Figure 4 for the individual nuclei are most likely due to statistical noise.

3.1.3. VLA Imaging

The morphology of the 8.46 GHz radio continuum emission in MCG-03-34-64 is also analyzed, and shown in Figure 5 . The positions of the two radio centroids identified by Schmitt et al. ( 2001 ) in the inner region are shown as white circles (see their Table 2). The 8.46 GHz emission starts as a linear structure at the position of the northern radio centroid, extending ∼100 pc southwestward to the central radio peak, and then bending southward in the direction of the southern [O iii ] centroid (Figure 5 ). Table 4 lists the positions and fluxes of the individual radio components, as measured in Schmitt et al. ( 2001 ). The separation between the two centroids is 116 ± 14 pc.

Figure 5.  VLA-A 8.46 GHz (3.6 cm) radio continuum image of MCG-03-34-64. White circles mark the position of the two radio peaks, and the [O iii ] centroids are shown in green. The image is shown in log scale.

Table 4.  VLA-A 3.6 cm Radio Continuum Image Decomposition—Position and Fluxes of Individual Components

Note. From Schmitt et al. ( 2001 ).

3.1.4. Astrometry Registration

We initially apply CIAO wavdetect 15 to the merged 0.3–7 keV Chandra image with a >5 σ detection threshold, detecting two faint sources near the edge of the chip array (via comparison with the Vizier source catalog). However, given their faintness, these are not suitable to use as a basis for astrometry correction.

We then create Chandra images in the narrow Fe K α band (6.2–6.6 keV), known to be dominated by nuclear emission in obscured sources (Fabbiano & Elvis 2024 ). Assuming that the radio emission in AGNs also originates from the innermost regions around the SMBH, we correct Chandra's absolute astrometry by aligning the emission peaks seen in the Fe K α band with those in the 8.46 GHz radio image. This alignment method has been used in similar studies, such as in Mrk 78 (Fornasini et al. 2022 ).

We apply a total shift of [Δx, Δy] = [0.1, 0.7] pixels to the Chandra/ACIS data, well within the absolute astrometry accuracy of the telescope. 16 Comparison with available HST observations confirms the accuracy of VLA's astrometry.

3.2. Spectroscopic Analysis

The overall spectral profile observed in the Chandra data is consistent across the earlier NuSTAR and XMM-Newton observations. All three spectra show an absorption feature at 6.8 keV, likely arising from Fe XXV absorption, and suggesting an outflow with v ∼ 5000 km s −1 , as noted in Miniutti et al. ( 2007 ). Below 3 keV, the soft X-ray emission is dominated by photoionized and collisionally ionized emission lines, from Ne, O, and Fe L ions (Miniutti et al. 2007 ).

3.2.1. XMM-Newton and NuSTAR Spectral Fitting

We proceed to model the X-ray spectrum of MCG-03-34-64 with MYTorus (Murphy & Yaqoob 2009 ), a physically motivated model built to describe the interaction of the emission from an X-ray point-source with a surrounding, and homogeneous torus of cold neutral material.

We fit the joint XMM+NuSTAR X-ray spectrum with a source model of the form:

A × TBabs × [ xstar × MYTZ × zpowerlw +( C × ( MYTS + MYTL × gsmooth )+ zpowerlaw _ soft + soft _ emiss )], where TBabs describes the absorption of emission by the Galactic column density, xstar is the photoionized absorber described previously in Miniutti et al. ( 2007 ), [ MYTZ × zpowerlw] describes the intrinsic continuum in transmission absorbed by torus, MYTS is the scattered (reflected) toroidal component off Compton-thick matter, MYTL is the associated Fe/Ni K α /K β line emission, and gsmooth accounts for some Gaussian broadening of the MYTorus line emission, where the upper limit on the line width is σ < 65 eV. The soft X-ray components are zpowerlaw _ soft , which is an unabsorbed scattered power-law component, and soft _ emiss , which is the sum of the photo and collisionally ionized emission components described by Miniutti et al. ( 2007 ). A is the cross normalization factor between NuSTAR and XMM ( A = 1.20 ± 0.05) and C is the offset between reflected/line components and intrinsic continuum components ( C is frozen at 1). The results of the fitting are shown in Figure 6 (center and right panels).

Given the quality and resolution of the X-ray observations used in this work, the spectra and fitting models employed in our analysis account for emission within the entire inner region of MCG-03-34-64, and cannot be performed separately for the individual Fe K α peaks uncovered in the Chandra imaging data. In this case, the resulting configuration suggested by MYTorus requires one where one is looking along the edge (Compton-thin line of sight) of a very Compton-thick absorber overall, which obscures both Fe K α regions.

We also note that the difference observed between the summed Fe K α luminosities derived from the Chandra imaging, ∼6.4 × 10 40 erg s −1 , and the total Fe K α luminosity yielded by the spectral fitting with MYTorus , L (6.2−6.6 keV) = 1.0 × 10 41 erg s −1 , may be attributed to line absorption by the absorber, which in turn implies a higher intrinsic X-ray luminosity, as yielded by the results of the spectral fit.

4. Discussion

The results presented in Section 3 reveal puzzling properties of the emission in MCG-03-34-64. Our imaging analysis identified three [O iii ]-emitting regions in the HST/ACS data, separated by 76 ± 8 and 79 ± 8 pc (Table 2 , Figure 2 ). In X-rays with Chandra/ACIS, two spatially resolved peaks of emission are observed in the narrow 6.2–6.6 keV Fe K α band, separated by 125 ± 21 pc (Table 3 , Figure 3 ). In the radio with VLA-A, Schmitt et al. ( 2001 ) previously identified two distinct radio cores in the 8.46 GHz continuum, separated by 116 ± 14 pc (Table 4 , Figure 5 ).

Figure 7 shows the Chandra/ACIS Fe K α image and the position of these multiwavelength centroids. The image is binned at one-eighth of the native ACIS-S pixel and smoothed with 1 kernel Gaussian.

Figure 7.  Chandra/ACIS-S merged Fe K (6.2–6.6 keV) image of MCG-03-34-64 (one-eighth subpixel, and smoothed with 1 kernel Gaussian). Optical centroids from HST/ACS are shown in red, VLA-A 8.46 GHz centroids in white, and Chandra/ACIS Fe K centroids in blue. The circle sizes reflect uncertainties in the position of the centroids.

4.1. Bolometric Luminosity

Table 5.  Joint XMM and NuSTAR Spectral Fitting Results from MYTorus

Notes. We use a correction factor k = 30 (Vasudevan & Fabian 2007 ) to obtain the integrated bolometric luminosity in X-rays.

4.1.1. HST F814W Continuum Fluxes

Our results reveal that the integrated observed fluxes are ≤3% of the integrated intrinsic flux in the band (Table 6 ), with the largest fraction originating from the central region. These fractions are consistent with scattered, hidden continuum (e.g., Pier et al. 1994 ), but could also include contributions from emission lines (e.g., Kraemer & Crenshaw 2000 ) and recombination continuum (Osterbrock & Robertis 1985 ). Therefore, it is unlikely that we will detect AGN continuum emission directly in the optical, and we cannot determine which of these regions harbors the AGN, given that all three regions are consistent with scatter continua from an active nucleus (but see below).

Table 6.  [O iii ] Luminosities Calculated from the Measured [O iii ] Fluxes from Table 2 , and Considering a Distance of D = 78 Mpc

Note We use a correction factor c = 454 (Lamastra et al. 2009 ) to calculate the bolometric luminosity in each [O iii ] region. We also show the measured F814W fluxes for each emitting region.

4.2. Multiwavelength Emission Centroids

The high fluxes and luminosities found in Section 3 for individual emission regions in the optical, X-ray, and radio bands support the presence of an AGN in this system. However, pinpointing the AGN's location is more challenging. We discuss possible interpretations for the system's configuration, based on our results and the limitations of the data.

4.2.1. Single AGN+Shocked Interstellar Medium

One interpretation is that the active nucleus is located at the position of the northern centroids ([O iii ], Fe K α , and radio; see Figure 7 ), based on the fluxes of individual components (Tables 2 , 3 , and 4 ). In this single AGN scenario, the remaining emission centroids (central Fe K α , [O iii ] and radio centroids, and southern [O iii ]) may arise from the interaction between the AGN and the interstellar medium (ISM). This would manifest as a mix of photoionized and collisionally ionized (shocked) gas from an extended NLR, similar to NGC 3393 (e.g., Maksym et al. 2016 ). Such an interpretation is consistent with previous spectral fitting results for this galaxy, which indicate a mix of photoionized and shock-ionized gas in the soft X-ray emission (Miniutti et al. 2007 ). Similarly, it is possible that the AGN in this system is located at the position of the central [O iii ], Fe K α , and radio peaks, while the remaining multiwavelength centroids may be attributed to AGN–ISM shock emission.

4.2.2. Dual AGN+Shocked ISM

Following the discussion in Section 4.2.1 , the high Fe K α luminosities (Table 3 ), and the high energies required for the production of such line emission suggest that both Fe K α regions could be powered by an active SMBH. In this scenario, the northern emission centroids would pinpoint the location of one AGN, while the central emission centroids (radio, Fe K α , and optical) may be associated with a second active SMBH in this system (given the high fluxes found for the central optical region in the F814W continuum band; Table 6 ). The distances measured between the different centroids attributed to each AGN are consistent across different wave bands (Table 7 ), supporting the dual AGN scenario.

Table 7.  Distances between Multiwavelength Centroids Found in This Work: HST/ACS, VLA-A, and Chandra/ACIS-S (Fe K α )

In this scenario, the southern [O iii ] region may arise from collisionally ionized emission in the ISM. Shock emission from jet–ISM interaction at the southern optical centroid location is supported by (1) the morphology of the radio emission at the central radio peak, which is observed to bend southward (Schmitt et al. 2001 ), in the direction of the southern [O iii ] peak (see Figures 5 and 3 , and Section 3.1.3 ); and (2) the morphology of the Chandra 0.3–3 keV (soft) emission, which is also observed to bend southward in the direction of the southern [O iii ] centroid (see Figure 3 , and Section 3.1.3 ). The lack of a corresponding southern radio or hard X-ray counterpart is consistent with the hypothesis that the northern and central [O iii ] centroids are powered by individual AGNs.

The dual AGN scenario is strengthened by the consistent values between the estimated bolometric luminosities derived from [O iii ] and X-rays (Tables 5 and 6 ), and the detection of nearly equal Fe K α emission peaks in the Chandra image, a powerful tool for identifying and confirming dual AGN systems (De Rosa et al. 2022 ). In the 3–5 keV Chandra image (Figure 3 ), the northern nucleus appears brighter than the central nucleus, although some extended emission is observed toward the central nucleus in this band. The column densities in the transmission of the two nuclei may not be the same, i.e., the AGN located at the central Fe K α region could have a higher absorbing column and appear fainter at lower energies. A higher contribution from the photoionized+thermal extended soft X-ray gas is expected at these lower energies. Given the resolution of the analyzed X-ray data, performing a separate spectral analysis of each individual Fe K α region is currently impractical.

5. Summary and Conclusions

We analyze new HST/ACS and Chandra/ACIS observations of the nearby Seyfert galaxy MCG-03-34-64, along with archival HST/ACS, XMM-Newton/Epic-pn, NuSTAR, and VLA-A data sets. Our analysis reveals the following:

In X-rays with Chandra: Two spatially resolved emission centroids are detected in the 6.2–6.6 keV Fe K α image, separated by 125 ± 21 pc. These peaks are evident in individual exposures and the merged data set. The northern and central Fe K α regions have 36 ± 6 and 37 ± 6 counts in the narrow 6.2–6.6 keV band, respectively, corresponding to ≥6 σ detections, and nearly equal Fe K α luminosities, L (6.2−6.6 keV) ∼ 3.2 ± 0.6 × 10 40 erg s −1 .

In the radio with VLA: Two emission regions are observed in the 3.6 cm VLA continuum image (Schmitt et al. 2001 ), spatially colocated with the northern and central Fe K α and [O iii ] regions.

We propose two possible physical interpretations of our results, and discuss these in the context of our analysis:

1. The "single AGN+shocked ISM" scenario, which proposes the existence of a single active nucleus in the system, while the remaining multiwavelength centroids may be attributed to the interaction of the ISM with the radio jet in the NLR.

This scenario is strengthened by:

a. Previous X-ray studies on this source, which find evidence for a mix of collisionally and photoionized X-ray gas in the NLR (Miniutti et al. 2007 ).

This scenario is challenged by:

b. The high Fe K α luminosities derived for individual regions and the energies required for the production of such line emission.

2. The "dual AGN+shocked ISM" scenario, which proposes the existence of a dual SMBH pair in this system separated by just 125 ± 21 pc.

a. The detection of two spatially resolved Fe K α regions in the Chandra imaging data, with high individual luminosities (Table 3 ).

b. The detection of three very bright and compact (<60 pc diameter) [O iii ]-emitting regions in the HST imaging data, and the respective individual bolometric luminosities (Table 6 ).

c. The detection of spatially coincident Fe K α , radio, and [O iii ] centroids at the northern and central regions. This is the first time spatially resolved, multiwavelength emission centroids in X-rays, radio, and optical are detected colocated in a nearby candidate dual AGN. For comparison, the recent study of Koss et al. ( 2023 ), which identified the presence of a dual AGN system separated by ∼230 pc in UGC 4211, detected colocated optical (HST F814W, MUSE AO [O iii ], and H α ), NIR (Keck J and K'), and submillimeter (Atacama Large Millimeter/submillimeter Array continuum at ∼230 GHz) centroids at the position of the two nuclei, but with no confirmation from X-rays or radio observations.

In summary, although we cannot definitively confirm or exclude the physical scenarios presented here, identification of the two nuclei in a deeper Chandra exposure would help to confirm a possible dual black hole system in this galaxy. Analysis of gas kinematics in the nuclear region of MCG-03-34-64 is crucial to determine the nature of the observed structures. Kinematic information obtained with HST/STIS long-slit spectroscopy could reveal disturbed kinematics expected from either the individual outflows of two SMBHs or the highly disturbed kinematics resulting from the merger environment. This information cannot be obtained from the archival X-Shooter data and requires the resolution of HST to probe the ∼100 pc region of interest.

https://ned.ipac.caltech.edu/

https://cxc.cfa.harvard.edu/ciao/

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GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.

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New tidally tilted pulsator discovered by astronomers

by Tomasz Nowakowski , Phys.org

Astronomers have detected a new tidally pulsator star (TTP) by analyzing the data from NASA's Transiting Exoplanet Survey Satellite (TESS). The newfound pulsator, designated TIC 435850195, belongs to the rare class of tri-axial TTPs. The finding was detailed in a research paper published September 5 on the pre-print server arXiv .

The so-called TTPs are pulsating stars in tight binaries in which the pulsation axes are tilted into the orbital plane by the tidal bulge induced by their companions. In general, the pulsation axis in such systems is aligned with the tidal bulge, rather than with the spin axis of the star.

TTPs are a rare find, as to date only a handful of these pulsators have been detected. Only one of them, known as TIC 184743498, exhibits pulsation along three different axes, making it a tri-axial pulsator (TAP).

Now, a team of astronomers led by Rahul Jayaraman of the Massachusetts Institute of Technology (MIT) reports the discovery of another TAP. By conducting a visual survey of light curves from the TESS full-frame images, they found that the eclipsing binary TIC 435850195 experiences TAP behavior.

"In this work, we report on the identification of the second-ever discovered tri-axial pulsator, with 16 robustly-detected pulsation multiplets, of which 14 are dipole doublets separated by 2 νorb ," the researchers wrote.

All in all, the study found that TIC 435850195 showcases 14 dipole doublet pulsations, two singlet pulsations, and two triplet pulsations—one dipole, and one quadrupole. The astronomers assume that the triplet dipole mode may not be fully tidally tilted, while the quadrupole mode is difficult to interpret with the currently available data.

Further investigation of the pulsation allowed the team to conclude that the observed multiplets are indeed caused by tidal phenomena. They excluded the possibility that these multiplets may be a function of the observational perspective on the system.

When it comes to the parameters of TIC 435850195, the researchers found that it consists of a slightly evolved primary Delta Scuti star and a secondary K-type star that is still on the zero-age main sequence. The system is estimated to be nearly one billion years old and is located some 1,750 light years away from the Earth.

Summing up the results, the authors of the paper noted that TIC 435850195 presents a wealth of observed pulsational behaviors. Due to this, the system is a unique laboratory through which the effects of a companion's gravitational field on the pulsations of a star can be thoroughly investigated.

The astronomers hope that future releases of TESS light curves will allow further detections of tri-axial pulsations in many more classes of stars.

Journal information: arXiv

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