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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.

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.

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 8 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.

affunlabeled
Aggregation of Reinforcement Learning Algorithms
Ju Jiang, Mohamed S. Kamel
2006· article· en· The 2006 IEEE International Joint Conference on Neural Network Proceedings· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Teaching with RoboCup
Jacky Baltes, Elizabeth Sklar, John Anderson
2004· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Efficient planning in R-max
Marek Grześ, Jesse Hoey
2011· article· en· Adaptive Agents and Multi-Agents Systems· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Reinforcement learning and aggregation
Ju Jiang, Mohamed S. Kamel, Liang-Bi Chen
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Multi-Agent Advisor Q-Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
2022· article· en· Journal of Artificial Intelligence Research· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Exploration Methods in Reinforcement Learning
Bingjie Shen
2022· article· en· 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
fundno affunlabeled
Planning With Pixels in (Almost) Real Time
Wilmer Bandres, Blai Bonet, Héctor Geffner
2018· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations

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