Evidence map›Paper›PMID 37426542›Full record

ArticleCell reports. Physical science2023

Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools.

Heather Desaire, Aleesa E Chua, Madeline Isom, Romana Jarosova, David Hua

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Physical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Heather DesaireDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
Aleesa E ChuaDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
Madeline IsomDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
Romana JarosovaDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.
David HuaDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.

Funding

Mass Spectrometry Measurements for Complex PTMsR35GM130354 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI DESAIRE, HEATHER R · 2019 to 2023
$1.9M
NIGMS NIH HHS R35 GM130354
6 · The paper itself

Abstract

ChatGPT has enabled access to artificial intelligence (AI)-generated writing for the masses, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent. Addressing this need, we report a method for discriminating text generated by ChatGPT from (human) academic scientists, relying on prevalent and accessible supervised classification methods. The approach uses new features for discriminating (these) humans from AI; as examples, scientists write long paragraphs and have a penchant for equivocal language, frequently using words like "but," "however," and "although." With a set of 20 features, we built a model that assigns the author, as human or AI, at over 99% accuracy. This strategy could be further adapted and developed by others with basic skills in supervised classification, enabling access to many highly accurate and targeted models for detecting AI usage in academic writing and beyond.

Identifiers

PMID37426542
PMCPMC10328544

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.