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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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Advanced Bandit Algorithms Research
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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.

436 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.
436 works in the cohort · of 4,299,418page 5 of 9

Labels cover 1 of 436 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 436 of 436 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.

affno abstractunlabeled
Strategic Manipulation of Empirical Tests
Wojciech Olszewski, Alvaro Sandroni
2008· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · metaresearch+research_integrityconsensus · none
4
citations
afffundunlabeled
Signal detection models as contextual bandits
Thomas N. Sherratt, Erica O’Neill
2023· article· en· Royal Society Open Science· Decision Sciences
machine prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
Sublinear regret for learning POMDPs
Yi Xiong, Ningyuan Chen, Xuefeng Gao, Xiang Zhou
2022· article· en· Production and Operations Management· Decision Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Budget-Driven Big Data Classification
Yiming Qian, Hao Yuan, Minglun Gong
2015· book-chapter· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Foundations of Information Theory
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Prediction by Random-Walk Perturbation
Luc Devroye, Gábor Lugosi, Gergely Neu
2013· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
SEH: Size Estimate Hedging Scheduling of Queues
Maryam Akbari‐Moghaddam, Douglas G. Down
2023· article· en· ACM Transactions on Modeling and Computer Simulation· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Learning Product Rankings Robust to Fake Users
Negin Golrezaei, Vahideh Manshadi, Jon Schneider, Shreyas Sekar
2020· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Foundations of Convex Analysis
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
One-armed bandit process with a covariate
You Liang, Xikui Wang, Yanqing Yi
2013· article· en· Annals of the Institute of Statistical Mathematics· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Learning Not to Regret
David Sychrovský, Michal Šustr, Elnaz Davoodi, Michael Bowling, Marc Lanctot, Martin Schmid
2024· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
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