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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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Reinforcement Learning in Robotics
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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.

1,145 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.
1,145 works in the cohort · of 4,299,418page 3 of 23

Labels cover 2 of 1,145 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 1,145 of 1,145 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
Ensemble reinforcement learning: A survey
Yanjie Song, Ponnuthurai Nagaratnam Suganthan, Witold Pedrycz, Junwei Ou, Yong‐Ming He, Yingwu Chen +1 more
2023· article· en· Applied Soft Computing· Computer Science
machine prediction:candidate · noneconsensus · none
54
citations
affunlabeled
Opposition-Based Q(λ) Algorithm
Maryam Shokri, Hamid R. Tizhoosh, Mohamed S. Kamel
2006· article· en· The 2006 IEEE International Joint Conference on Neural Network Proceedings· Computer Science
machine prediction:candidate · noneconsensus · none
48
citations
afffundunlabeled
The Reinforcement Learning Competitions
Shimon Whiteson, B. K. Tanner, Adam White
2010· article· en· AI Magazine· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affno abstractunlabeled
Model selection in reinforcement learning
Amir‐massoud Farahmand, Csaba Szepesvári
2011· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affno abstractunlabeled
Reinforcement Learning
Ke-Lin Du, M. N. S. Swamy
2019· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
iLSTD: Eligibility Traces and Convergence Analysis
Alborz Geramifard, Michael Bowling, Martin Zinkevich, Richard S. Sutton
2007· book-chapter· en· The MIT Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
42
citations

How this was built: Screen · Findings · About