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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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Advanced Statistical Methods and Models
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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.

1,193 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.
1,193 works in the cohort · of 4,299,418page 17 of 24

Labels cover 6 of 1,193 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 1,193 of 1,193 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.

affno abstractunlabeled
Statistical Analysis
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Tests for assessing vector correlation
Hamani El Maâche, Yves Lepage
2007· article· en· Model Assisted Statistics and Applications· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Projection de hájek et polynômes de bernstein
Marc Hallin, Amal Mellouk, Khalid Rifi
2001· article· fr· Canadian Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
One Sided Tolerance Limits Via Smoothing
W. John Braun, Lutong Zhou
2008· article· en· Quality Technology & Quantitative Management· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
A Model Selection Method for S-Estimation
Arie Preminger, Shinichi Sakata
2005· article· en· SSRN Electronic Journal· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Data Fusion Using Empirical Likelihood
Hsiao‐Hsuan Wang, Yuehua Wu, Yuejiao Fu, Xiaogang Wang
2012· article· en· Open Journal of Statistics· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Calculating the simplex median
Xin Huang, Christopher G. Small
2004· article· en· Statistics and Computing· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Less Parametric Methods in Statistics
K. Laurence Weldon
2005· article· en· Repository of the University of Ljubljana (University of Ljubljana)· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Aggregation Strategies
2018· book-chapter· en· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About