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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.

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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 10 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.

afffundno abstractunlabeled
Online Learning for Two Novel Latent Topic Models
Ali Bakhtiari, Nizar Bouguila
2014· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
afffundno abstractunlabeled
A copula model for marked point processes
Liqun Diao, Richard J. Cook, Ker‐Ai Lee
2013· article· en· Lifetime Data Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Kendall’s tau for hierarchical data
Héla Romdhani, Lajmi Lakhal‐Chaieb, Louis‐Paul Rivest
2014· article· de· Journal of Multivariate Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Mixture models: building a parameter space
Vahed Maroufy, Paul Marriott
2016· article· en· Statistics and Computing· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Methods of moments estimation in finite mixtures
Paul J. Farrell, A. K. Md. Ehsanes Saleh, Zhengmin Zhang
2011· article· en· Sankhya A· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Bayesian clustering of many GARCH models
Luc Bauwens, Jeroen V.K. Rombouts
2003· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
Alcock–Paczyński effect on void-finding
Slađana Radinović, Hans A. Winther, S. Nadathur, Will J. Percival, E. Paillas, Tristan Sohrab Fraser +2 more
2024· article· en· Astronomy and Astrophysics· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Multistage Cluster Sampling
John H. Duffus, Monica Nordberg, Douglas M. Templeton
2016· dataset· en· IUPAC Standards Online· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
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How this was built: Screen · Findings · About