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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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Bayesian Methods and Mixture 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.

1,238 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.
1,238 works in the cohort · of 4,299,418page 21 of 25

Labels cover 4 of 1,238 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 1,238 of 1,238 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundunlabeled
A mixture model for skewed mixed-type data
Eman Mohammed S. Alamer, Michael P. B. Gallaugher, Paul D. McNicholas
2025· article· en· Statistics & Probability Letters· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affunlabeled
A product formula for the TASEP on a ring
Erik Aas, Jonas Sjöstrand
2014· article· fr· Discrete Mathematics & Theoretical Computer Science· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communicationconsensus · sts
0
citations
affunlabeled
On Smoothed MWSD Estimation of Mixing Proportion
Satish Konda, K. L. Mehra, Ramakrishnaiah Y.S.
2021· article· en· Pakistan Journal of Statistics and Operation Research· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Loss modelling with mixtures of Erlang distributions
Roel Verbelen, Liutang Gong, Katrien Antonio, Andrei L. Badescu, Lorraine Sheldon
2014· article· en· Lirias (KU Leuven)· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Exact sampling with highly uniform point sets
Christiane Lemieux, Paul Sidorsky
2006· article· en· Mathematical and Computer Modelling· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Dirichlet’s theorem
Jean–Marie De Koninck, Nicolas Doyon
2021· book-chapter· en· American Mathematical Society eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
affunlabeled
Some recent statistical methods applied in genetics/genomics
Gilles Durrieu, Laurent Briollais
2017· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
0
citations
affunlabeled
MCMC for Imbalanced Categorical Data
James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson
2018· preprint· en· Journal of the American Statistical Association· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About