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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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Applied Stochastic Models in Business and Industry
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Retraction
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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.

104 results · 1 filter active ·
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20022025
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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.
104 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 104 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 104 of 104 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

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
distilled prediction:candidate · noneconsensus · none
65
citations
afffundunlabeled
A risk model driven by Lévy processes
Manuel Morales, Wim Schoutens
2003· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
distilled prediction:candidate · metaepi_narrowconsensus · none
32
citations
affunlabeled
American option prices in a Markov chain market model
John van der Hoek, Robert J. Elliott
2011· article· en· Applied Stochastic Models in Business and Industry· Economics, Econometrics and Finance
distilled prediction:candidate · metaepi_narrowconsensus · none
24
citations
affunlabeled
Multivariate risk models under heavy‐tailed risks
Wei Huang, Chengguo Weng, Yi Zhang
2013· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
distilled prediction:candidate · metaepi_narrowconsensus · none
22
citations
affunlabeled
Multivariate option pricing using copulae
Carole Bernard, Claudia Czado
2012· article· en· Applied Stochastic Models in Business and Industry· Economics, Econometrics and Finance
distilled prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Score tests for inverse Gaussian mixtures
Anthony F. Desmond, Zhenlin Yang
2010· article· en· Applied Stochastic Models in Business and Industry· Mathematics
distilled prediction:candidate · noneconsensus · none
11
citations
affunlabeled
A method for portfolio choice
Robert J. Elliott, Juri Hinz
2003· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
distilled prediction:candidate · noneconsensus · none
9
citations
affunlabeled
A real‐time monitoring approach for bivariate event data
Inez Maria Zwetsloot, Tahir Mahmood, Funmilola Mary Taiwo, Zezhong Wang
2023· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
distilled prediction:candidate · noneconsensus · none
9
citations
fundno affunlabeled
Advancing public sector performance analysis
Carolyn J. Heinrich
2008· article· en· Applied Stochastic Models in Business and Industry· Social Sciences
distilled prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
Mean‐variance hedging with basis risk
Xiaole Xue, Jinggong Zhang, Chengguo Weng
2018· article· en· Applied Stochastic Models in Business and Industry· Economics, Econometrics and Finance
distilled prediction:candidate · noneconsensus · none
5
citations

How this was built: Screen · Findings · About