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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Research Integrity and Peer Review
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Retraction
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

29 results · 1 filter active ·
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20162025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
29 works in the cohort · of 4,299,418page 1 of 1

Labels cover 18 of 29 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 29 of 29 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affgemma · metaresearchgpt · no categorymodels split
Improving equity, diversity, and inclusion in academia
Omar Dewidar, Nour Elmestekawy, Vivian Welch
2022· letter· en· Research Integrity and Peer Review· Social Sciences
machine prediction:candidate · metaresearch+open_scienceconsensus · none
98
citations
affunlabeled
Retractions in cancer research: a systematic survey
Anthony Bozzo, Kamal Bali, Nathan Evaniew, Michelle Ghert
2017· article· en· Research Integrity and Peer Review· Social Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · none
83
citations
affgemma · metaresearchgpt · metaresearchmodels split
Updating standards for reporting diagnostic accuracy: the development of STARD 2015
Daniël A. Korevaar, Jérémie F. Cohen, Johannes B. Reitsma, David E. Bruns, Constantine Gatsonis, Paul Glasziou +6 more
2016· article· en· Research Integrity and Peer Review· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
82
citations
affunlabeled
Quantifying professionalism in peer review
Travis G. Gerwing, Alyssa M. Allen Gerwing, Stephanie Avery‐Gomm, Chi‐Yeung Choi, Jeff C. Clements, Joshua A. Rash
2020· article· en· Research Integrity and Peer Review· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · metaresearch
69
citations
affgemma · metaresearchgpt · metaresearchmodels agree
Improving the process of research ethics review
Stacey Page, Jeffrey Nyeboer
2017· editorial· en· Research Integrity and Peer Review· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · none
67
citations
affgemma · metaresearchgpt · metaresearch+research_integrity+scholarly_communicationmodels split
Re-evaluation of solutions to the problem of unprofessionalism in peer review
Travis G. Gerwing, Alyssa M. Allen Gerwing, Chi‐Yeung Choi, Stephanie Avery‐Gomm, Jeff C. Clements, Joshua A. Rash
2021· article· en· Research Integrity and Peer Review· Decision Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
17
citations
affgemma · metaresearch+bibliometricsgpt · metaresearch+bibliometricsmodels agree
Characteristics of ‘mega’ peer-reviewers
Danielle B. Rice, Ba’ Pham, Justin Presseau, Andrea C. Tricco, David Moher
2022· article· en· Research Integrity and Peer Review· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
17
citations
affgemma · research_integritygpt · research_integritymodels agree
Proceedings of the 4th World Conference on Research Integrity
Susan Patricia O’Brien, Danny Chan, Fks Leung, Eun Jung Ko, Jin Sun Kwak, TaeHwan Gwon +298 more
2016· article· en· Research Integrity and Peer Review· Medicine
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
12
citations
affgemma · metaresearch+research_integritygpt · metaresearch+research_integritymodels agree
Research on policy mechanisms to address funding bias and conflicts of interest in biomedical research: a scoping review
S. Scott Graham, Quinn Grundy, Nandini Sharma, Joshua B. Barbour, Justin F. Rousseau, Zoltan P. Majdik +1 more
2025· review· en· Research Integrity and Peer Review· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
6
citations
fundno affgemma · metaresearch+research_integritygpt · metaresearch+research_integrity+open_sciencemodels split
Steps toward preregistration of research on research integrity
Klaas Sijtsma, Wilco H. M. Emons, Nicholas H. Steneck, L.M. Bouter
2021· article· en· Research Integrity and Peer Review· Social Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
6
citations
affgemma · metaresearchgpt · metaresearchmodels agree
Correction: Characteristics of ‘mega’ peer-reviewers
Danielle B. Rice, Ba’ Pham, Justin Presseau, Andrea C. Tricco, David Moher
2022· erratum· en· Research Integrity and Peer Review· Social Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · none
0
citations

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