Springer Nature retracts book with fake citations. Help us find more cases like this.

Springer Nature has officially retracted a book on machine learning following coverage by Retraction Watch. A reader sent us a tip about this book; we’d love your help identifying more.

As we reported, the book, Mastering Machine Learning: From Basics to Advanced, contained many citations to nonexistent works. These fake references are a hallmark of text generated by large language models like ChatGPT. 

The retraction notice mentions the illusory citations, stating, “Following publication concerns were raised regarding the validity of certain references. Upon further investigation, the Publisher was unable to verify the source of 25 out of 46 references in this book.” After listing the 25 citations, 12 of which we found in our initial reporting, it continues, “the Series Editor and the publisher therefore no longer have confidence in the reliability of this book. The author has not stated explicitly whether he agrees with this retraction.”

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Misconduct investigation at U.S. military university uncovers image duplication

Authors affiliated with a federal health sciences university have lost three papers this year for image duplication following an investigation by the institution. And another journal has confirmed it will retract a fourth paper by some of the same authors.

The “internal research misconduct investigation” conducted by the Uniformed Services University, or USU, in Bethesda, Md., found “several falsified or inappropriately duplicated images” and “images from previously published articles,” according to two of the retraction notices. USU, an institution focused on military medicine and part of the U.S. Department of Defense, acknowledged our multiple requests for comment about the investigation but did not provide a statement.

In January, Retraction Watch filed a Freedom of Information Act request about the investigation. The Department of Defense acknowledged our request on January 7, noting the agency has 4,552 open requests that are processed in the order in which they are received.

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Microbiome company CEO who linked COVID vaccine to bacterial decline now has four retractions

A gastroenterologist and microbiome researcher who has promoted hydroxychloroquine and ivermectin as COVID treatments has lost a paper after a sleuth reported differences between the article and the registered protocol of the clinical trial it purported to describe. 

The retracted article, on detecting SARS-CoV-2 in fecal samples, was published in Gut Pathogens in January 2021. It marks the fourth retraction for study coauthor Sabine Hazan.

Hazan is founder and CEO of ProgenaBiome, a laboratory in Ventura, Calif., advertising clinical trials exploring the role of the gut microbiome in conditions including autism and Alzheimer’s disease, as well as COVID. She is also CEO of Ventura Clinical Trials, which lists dozens of pharmaceutical and biotech companies among its clients.  

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Springer Nature to retract machine learning book following Retraction Watch coverage

A screenshot from June 26 shows the book had been accessed 3,782 times.

Springer Nature is retracting a book on machine learning that had multiple references to works that do not exist, Retraction Watch has learned. 

The move comes two weeks after we reported on the book’s fake references.

The link to the information page for the book, Mastering Machine Learning: From Basics to Advanced, now returns “Page not found,” and the text is no longer listed under the book series on computer systems and networks. 

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Wake Forest cancer lab blames ‘honest mistakes’ for retractions

A prominent cancer research lab is up to three retractions and six corrections for “highly similar” images in papers published between 2018 and 2022. 

The lab is led by Kounosuke Watabe at Wake Forest University School of Medicine in Winston-Salem, North Carolina. Watabe holds one of three Wake Forest professorships all funded by a $2.8 million donation for cancer research in 2016. 

Each of the retractions and corrections came after sleuth Kevin Patrick raised concerns about the articles on PubPeer in May 2024. Patrick, who identified instances of images in Watabe lab papers being “more similar than expected,” told Retraction Watch he wasn’t confident whether the image duplication could be attributed to misconduct. “I am never sure which is worse, misconduct or a pattern of errors. Neither seem to inspire confidence in the published results,” he said. 

Watabe did not respond to multiple requests for comment.

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Do you need informed consent to study public posts on social media? 

The retraction of a paper looking at posts in a Reddit subforum about mental illness has once again raised questions about informed consent in research using public data. 

To study the experience of receiving a diagnosis of schizophrenia, a U.K.-based team of researchers collected posts from the Reddit subforum r/schizophrenia, which is dedicated to discussing the disorder. They analyzed and anonymized the data, and published their findings in June 2024 in Current Psychology, a Springer Nature journal. 

The paper prompted backlash on X in the subsequent months, and in the Reddit community used for the study. People on the subreddit were concerned about the lack of consent, potential lack of anonymity, and the hypocrisy of discussing ethics in the paper while not seeking consent, a moderator of that subreddit who goes by the handle Empty_Insight told Retraction Watch.

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Springer Nature book on machine learning is full of made-up citations

Would you pay $169 for an introductory ebook on machine learning with citations that appear to be made up?

If not, you might want to pass on purchasing Mastering Machine Learning: From Basics to Advanced, published by Springer Nature in April. 

Based on a tip from a reader, we checked 18 of the 46 citations in the book. Two-thirds of them either did not exist or had substantial errors. And three researchers cited in the book confirmed the works they supposedly authored were fake or the citation contained substantial errors.

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