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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.

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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 15 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. 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
Discrete Multivariate Distributions
N. Balakrishnan
2005· other· en· Encyclopedia of Statistical Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Population Monte Carlo With Normalizing Flow
Soumyasundar Pal, Antonios Valkanas, Mark Coates
2023· article· en· IEEE Signal Processing Letters· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundno abstractunlabeled
Three skewed matrix variate distributions
Michael P. B. Gallaugher, Paul D. McNicholas
2018· preprint· en· Statistics & Probability Letters· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Densities for random balanced sampling
Peter Bubenik, John Holbrook
2006· article· en· Journal of Multivariate Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Extremal problems on probability distributions
E.A. Galperin, Nikolay M. Yanev
2000· article· en· Mathematical and Computer Modelling· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundno abstractunlabeled
Kernel-based mixture models for classification
Alejandro Murua, Nicolas Wicker
2014· article· en· Computational Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Geometry in statistics
Paul Vos, Paul Marriott
2010· review· en· Wiley Interdisciplinary Reviews Computational Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Gradient and Likelihood Ratio Tests in Cure Rate Models
Hérica P. A. Carneiro, Dione Maria Valença
2016· article· en· International Journal of Statistics and Probability· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Chimeral Clustering
Jason Hou-Liu, Ryan P. Browne
2021· article· en· Journal of Classification· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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