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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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Statistics Education and Methodologies
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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.

affaffiliation
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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.

595 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
595 works in the cohort · of 4,299,418page 2 of 12

Labels cover 5 of 595 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 595 of 595 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.

afffundunlabeled
Statistical method use in public health research
James C. Karran, Erica E. M. Moodie, Michael P. Wallace
2015· review· en· Scandinavian Journal of Public Health· Mathematics
machine prediction:candidate · metaresearchconsensus · none
23
citations
affunlabeled
What Does the Mean Mean?
Nicholas Watier, Claude Lamontagne, Sylvain Chartier
2011· article· en· Journal of Statistics Education· Mathematics
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Editorial
Candace Schau, MICHELE MILLAR, Peter Petocz
2012· editorial· en· Statistics Education Research Journal· Mathematics
machine prediction:candidate · noneconsensus · none
17
citations
afffundunlabeled
Improving conceptual learning via pretests.
Faria Sana, Veronica X. Yan, Courtney M. Clark, Elizabeth Ligon Bjork, Robert A. Bjork
2020· article· en· Journal of Experimental Psychology Applied· Mathematics
machine prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
Inferential statistics and librarianship
Juris Dilevko
2007· article· en· Library & Information Science Research· Mathematics
machine prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
Editorial: (Post-normal) Statistical Science
James V. Zidek
2005· editorial· en· Journal of the Royal Statistical Society Series A (Statistics in Society)· Mathematics
machine prediction:candidate · metaresearchconsensus · none
12
citations
affno abstractunlabeled
Conceptual thinking and metrology concepts
Uri Shafrir, Ron S. Kenett
2010· article· en· Accreditation and Quality Assurance· Mathematics
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Mean likelihood estimators
A. Ian McLeod, Benoît Quenneville
2001· article· en· Statistics and Computing· Mathematics
machine prediction:candidate · noneconsensus · none
11
citations
venueno affgemma · no categorygpt · no categorymodels agree
Letter to the Editors
Panagiotis Tsigaris
2019· letter· en· Journal of Scholarly Publishing· Mathematics
machine prediction:candidate · insufficient_payloadconsensus · none
11
citations
aboutno affunlabeled
Research in Action: Taking Classroom Learning to the Field
Abigail Evans, Eliza T. Dresang, Katie Campana, Erika N. Feldman
2013· article· en· Journal of Education for Library and Information Science· Mathematics
machine prediction:candidate · noneconsensus · none
10
citations

How this was built: Screen · Findings · About