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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 and numerical algorithms
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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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aboutaboutness

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

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.
222 works in the cohort · of 4,299,418page 3 of 5

Labels cover 1 of 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 222 of 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
Error Estimation
Nathalie Japkowicz, Mohak Shah
2011· book-chapter· en· Cambridge University Press eBooks· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Gaussianity of the QMASK Map
S. F. Shandarin, Hume A. Feldman, Yongzhong Xu, Max Tegmark
2001· article· en· arXiv (Cornell University)· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
On distributions of covariance structures
A. M. Mathai, Nicy Sebastian
2022· article· en· Communication in Statistics- Theory and Methods· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
On regression analysis with Padé approximants
Glib Yevkin, Olexandr Yevkin
2023· article· en· Communication in Statistics- Theory and Methods· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Multivariate Approximation 2013
Marco Caliari, Stefano De Marchı, N. Levenberg, Marco Vianello
2014· article· en· Dolomites Research Notes on Approximation· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Resummation and hard thermal loops
Joseph I. Kapusta, Charles Gale
2023· book-chapter· en· Cambridge University Press eBooks· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Approximation
Alex Gezerlis
2023· book-chapter· en· Cambridge University Press eBooks· Mathematics
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
aboutno affunlabeled
Dictya montana Steyskal 1954
2023· article· en· Open MIND· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
RKHS Weightings of Functions
Gabriel Dubé, Mario Marchand
2023· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
9781003454342_10.4324_9781003454342-7.pdf
2024· other· en· OAPEN (The OAPEN Foundation)· Mathematics
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Data Processing
Professor Reda Alhajj, Professor Jon Rokne
2014· book-chapter· en· Mathematics
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About