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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 15 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
Turning lights out with DQ-learning
D.V. Batalov, B. John Oommen
2006· article· en· International conference on Artificial intelligence and applications· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Adaptive Approximate Policy Iteration
Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori, Pooria Joulani, Csaba Szepesvári
2021· article· en· International Conference on Artificial Intelligence and Statistics· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Towards Robust Bisimulation Metric Learning
Mete Kemertas, Tristan Aumentado‐Armstrong
2021· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
affno abstractunlabeled
Learning to Drive via Asymmetric Self-Play
Wenjun Zhang, Sourav Biswas, Kelvin Wong, Kion Fallah, Lunjun Zhang, Dian Chen +2 more
2024· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · none
1
citations
affunlabeled
Off-Policy Actor-Critic with Emphatic Weightings
Eric Graves, Ehsan Imani, Raksha Kumaraswamy, Martha White
2021· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · 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
2020· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
afffundunlabeled
Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi, Jun Jin, Jun Luo, A. Rupam Mahmood, Martin Jägersand, Samuele Tosatto
2023· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
affno abstractunlabeled
A Neural Field Approach to Obstacle Avoidance
Chun Kwang Tan, Paul G. Plöger, Thomas Trappenberg
2016· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
affno abstractunlabeled
Trustworthy Embodied Virtual Agents
Nathan Lloyd, Arsh Chowdhry, Peter R. Lewis
2023· book-chapter· en· Encyclopedia of Computer Graphics and Games· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations

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