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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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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.

1,914 results · 1 filter active ·
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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.
1,914 works in the cohort · of 4,299,418page 14 of 39

Labels cover 1,914 of 1,914 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 1,914 of 1,914 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 · metaresearch+research_integritygpt · metaresearch+open_science+research_integritymodels split
IMPACT Observatory: tracking the evolution of clinical trial data sharing and research integrity
Karmela Krleža-Jerić, Mirko Gabelica, Rita Banzi, Marina Krnić-Martinić, Bibiana Pulido, Mersiha Mahmić-Kaknjo +4 more
2016· review· en· Biochemia Medica· Computer Science
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
19
citations
affgemma · metaresearch+research_integritygpt · metaresearch+research_integritymodels agree
The Dynamics of Retraction in Epistemic Networks
Travis LaCroix, Anders Geil, Cailin O’Connor
2020· article· en· Philosophy of Science· Social Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · none
19
citations
affgemma · metaresearch+research_integritygpt · metaresearch+research_integritymodels agree
Requirements of health policy and services journals for authors to disclose financial and non-financial conflicts of interest: a cross-sectional study
Assem M. Khamis, Maram B Hakoum, Lama Bou-Karroum, Joseph R. Habib, Ahmed Ali, Gordon Guyatt +2 more
2017· article· en· Health Research Policy and Systems· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearch+research_integrityconsensus · none
19
citations
affno abstractgemma · bibliometricsgpt · bibliometrics+metaresearchmodels split
Neophilia ranking of scientific journals
Mikko Packalén, Jay Bhattacharya
2016· article· en· Scientometrics· Decision Sciences
machine prediction:candidate · bibliometricsconsensus · none
18
citations
affvenuegemma · metaresearchgpt · no categorymodels split
A Content Analysis of Systematic Review Online Library Guides
Jennifer Lee, Alix Hayden, Heather Ganshorn, Helen Pethrick
2021· article· en· Evidence Based Library and Information Practice· Decision Sciences
machine prediction:candidate · metaresearch+scholarly_communicationconsensus · none
18
citations
afffundaboutgemma · no categorygpt · metaresearchmodels split
One size doesn’t fit all: methodological reflections in conducting community-based behavioural science research to tailor COVID-19 vaccination initiatives for public health priority populations
Guillaume Fontaine, Maureen A. Smith, Tori Langmuir, Karim Mekki, Hanan Ghazal, Elizabeth Estey Noad +16 more
2024· article· en· BMC Public Health· Health Professions
machine prediction:candidate · metaresearchconsensus · metaresearch
18
citations
affno abstractgemma · metaresearchgpt · metaresearch+stsmodels split
Funding, objectivity and the socialization of medical research
James Robert Brown
2002· review· en· Science and Engineering Ethics· Pharmacology, Toxicology and Pharmaceutics
machine prediction:candidate · metaresearch+stsconsensus · none
18
citations
affgemma · no categorygpt · metaresearchmodels split
Analysis of Videotaped Data: Methodological Considerations
Janice M. Morse, Charlotte Pooler
2002· article· en· International Journal of Qualitative Methods· Social Sciences
machine prediction:candidate · metaresearchconsensus · none
18
citations
afffundgemma · metaresearchgpt · no categorymodels split
Aiming for quality: a global compass for national learning systems
Diana Sarakbi, Nana Mensah-Abrampah, Melissa Kleine-Bingham, Shams B. Syed
2021· review· en· Health Research Policy and Systems· Health Professions
machine prediction:candidate · noneconsensus · none
17
citations
affgemma · metaresearchgpt · metaresearch+stsmodels split
Co-production of evidence for policies in Thailand: from concept to action
Viroj Tangcharoensathien, Supakit Sirilak, Piyamitr Sritara, Walaiporn Patcharanarumol, Angkana Lekagul, Wanrudee Isaranuwatchai +2 more
2021· article· en· BMJ· Economics, Econometrics and Finance
machine prediction:candidate · metaresearch+stsconsensus · none
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
aboutno affgemma · metaresearchgpt · no categorymodels split
Human Research Ethics Committees in Technical Universities
David Koepsell, Willem‐Paul Brinkman, Sylvia C. Pont
2014· article· en· Journal of Empirical Research on Human Research Ethics· Engineering
machine prediction:candidate · metaresearch+research_integrityconsensus · metaresearch
17
citations
affgemma · metaresearchgpt · no categorymodels split
Turning the crank for machine learning: ease, at what expense?
Tom Pollard, Irene A. Chen, Jenna Wiens, Steven Horng, D. J. N. Wong, Marzyeh Ghassemi +3 more
2019· letter· en· The Lancet Digital Health· Medicine
machine prediction:candidate · noneconsensus · none
17
citations

How this was built: Screen · Findings · About