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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.

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.

266 results · 1 filter active ·
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20002025
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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.
266 works in the cohort · of 4,299,418page 1 of 6

Labels cover 0 of 266 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 266 of 266 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.

fundno affunlabeled
Estimating the Encounter Rate Variance in Distance Sampling
Rachel M. Fewster, S. T. Buckland, Kenneth P. Burnham, David L. Borchers, Peter E. Jupp, Jeffrey L. Laake +1 more
2008· article· en· Biometrics· Mathematics
machine prediction:candidate · noneconsensus · none
162
citations
affunlabeled
Adaptive Web Sampling
Steven K. Thompson
2006· article· en· Biometrics· Mathematics
machine prediction:candidate · noneconsensus · none
79
citations
affunlabeled
PICS: Probabilistic Inference for ChIP-seq
Xuekui Zhang, Gordon Robertson, Martin Krzywinski, Kaida Ning, Arnaud Droit, Steven J.M. Jones +1 more
2010· article· en· Biometrics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
71
citations
afffundunlabeled
The Jolly–Seber Model with Tag Loss
Laura Cowen, Carl J. Schwarz
2006· article· en· Biometrics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
57
citations
affunlabeled
Multiscale Processing of Mass Spectrometry Data
Timothy W. Randolph, Yutaka Yasui
2006· article· en· Biometrics· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
53
citations
afffundaboutunlabeled
Estimation in Bayesian Disease Mapping
Ying C. MacNab, Paul J. Farrell, Paul Gustafson, Sijin Wen
2004· article· en· Biometrics· Mathematics
machine prediction:candidate · noneconsensus · none
45
citations

How this was built: Screen · Findings · About