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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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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
fundfunder
venuejournal
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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 16 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Reinforcement Learning
Daniel J. Lizotte
2017· other· en· Wiley StatsRef: Statistics Reference Online· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
1
citations
venueno affunlabeled
LongiControl: A New Reinforcement Learning Environment
Roman Ließner, Jan Dohmen, Christoph Friebel, Bernard Bäker
2019· article· en· Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li +1 more
2021· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
SWL11 Bottle data. Version 1.0
2015· dataset· en· Earth Observing Laboratory· Computer Science
distilled prediction:candidate · metaepi_narrow+open_science+insufficient_payloadconsensus · none
1
citations
fundno affno abstractunlabeled
REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
Le Zhang, Bo Wang, Xipeng Qiu, Siva Reddy, Aishwarya Agrawal
2025· article· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+insufficient_payloadconsensus · insufficient_payload
1
citations
affunlabeled
Learning One Representation to Optimize All Rewards
Abdelaziz Touati, Yann Ollivier
2021· article· en· Neural Information Processing Systems· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
1
citations
affno abstractunlabeled
Exploration-Driven Representation Learning in Reinforcement Learning
Akram Erraqabi, Mingde Zhao, Marlos C. Machado, Yoshua Bengio, Sainbayar Sukhbaatar, Ludovic Denoyer +1 more
2021· article· en· International Conference on Machine Learning· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
affunlabeled
Learning multi-step predictive state representations
Lucas Langer, Borja Balle, Doina Precup
2016· article· en· International Joint Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
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