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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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Probability and Risk 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.

682 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.
682 works in the cohort · of 4,299,418page 1 of 14

Labels cover 0 of 682 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 682 of 682 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
A Primer on Copulas for Count Data
Christian Genest, Johanna Nešlehová
2007· article· en· Astin Bulletin· Decision Sciences
machine prediction:candidate · noneconsensus · none
310
citations
affunlabeled
Risk Classification for Claim Counts
Jean‐Philippe Boucher, Michel Denuit, Montserrat Guillén
2007· article· en· North American Actuarial Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
112
citations
affunlabeled
[no title]
Andreas E. Kyprianou, Juan Carlos Pardo, Kees van Schaik
2011· article· en· The University of Bath Online Publications Store (The University of Bath)· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
94
citations
affunlabeled
Rare events, splitting, and quasi-Monte Carlo
Pierre L’Ecuyer, Valérie Demers, Bruno Tuffin
2007· article· en· ACM Transactions on Modeling and Computer Simulation· Decision Sciences
machine prediction:candidate · noneconsensus · none
89
citations
afffundunlabeled
A Lévy Insurance Risk Process with Tax
Hansjörg Albrecher, Jean‐François Renaud, Xiaowen Zhou
2008· article· en· Journal of Applied Probability· Decision Sciences
machine prediction:candidate · noneconsensus · none
87
citations
affunlabeled
Collective Risk Theory
Cary Chi‐Liang Tsai
2004· other· en· Encyclopedia of Actuarial Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
83
citations
affno abstractunlabeled
Multivariate risk model of phase type
Jun Cai, Haijun Li
2005· article· en· Insurance Mathematics and Economics· Decision Sciences
machine prediction:candidate · noneconsensus · none
76
citations
afffundno abstractunlabeled
On two dependent individual risk models
Hélène Cossette, Patrice Gaillardetz, Étienne Marceau, Jacques E. Rioux
2002· article· en· Insurance Mathematics and Economics· Decision Sciences
machine prediction:candidate · noneconsensus · none
71
citations
afffundunlabeled
Risk modelling with the mixed Erlang distribution
Gordon E. Willmot, Xiaodong Lin
2010· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
machine prediction:candidate · noneconsensus · none
65
citations
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