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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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Statistical Methods and Bayesian Inference
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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
fundfunder
venuejournal
aboutaboutness

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

1,222 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.
1,222 works in the cohort · of 4,299,418page 18 of 25

Labels cover 16 of 1,222 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 1,222 of 1,222 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.

affunlabeled
Overdispersion
C. B. Dean
2008· other· en· Wiley Encyclopedia of Clinical Trials· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Sensitivity Analysis: Introduction
Charles H. Goldsmith
2016· other· en· Wiley StatsRef: Statistics Reference Online· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
afffundvenueaboutunlabeled
On the use of priors in goodness‐of‐fit tests
Alberto Contreras‐Cristán, Richard Lockhart, Michael A. Stephens, Shaun Zheng Sun
2019· article· en· Canadian Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
CamDavidsonPilon/lifelines: v0.22.0
Cameron Davidson-Pilon, Jonas Kalderstam, Paul N. Zivich, Ben Kuhn, Andrew Fiore-Gartland, Luis Moneda +24 more
2019· article· en· Zenodo (CERN European Organization for Nuclear Research)· Mathematics
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affunlabeled
Errors in the Measurement of Covariates
Grace Y. Yi, Richard J. Cook
2014· other· en· Wiley StatsRef: Statistics Reference Online· Mathematics
machine prediction:candidate · metaresearchconsensus · none
1
citations
affno abstractunlabeled
Discussion
Alexandre Bouchard‐Côté, James V. Zidek
2012· article· en· International Statistical Review· Mathematics
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affunlabeled
Model Analysis
2003· other· en· Wiley series in probability and statistics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Longitudinal Mixed Models for Count Data
Brajendra C. Sutradhar
2011· book-chapter· en· Springer series in statistics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Methods for Handling Missing Data
Changbao Wu, Mary E. Thompson
2020· book-chapter· en· ICSA book series in statistics· Mathematics
machine prediction:candidate · metaresearchconsensus · none
1
citations

How this was built: Screen · Findings · About