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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 9 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
NeuroHex: A Deep Q-learning Hex Agent
Kenny Young, Gautham Vasan, Ryan Hayward
2017· preprint· en· Communications in computer and information science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Reward shaping using convolutional neural network
Hani Sami, Hadi Otrok, Jamal Bentahar, Azzam Mourad, Ernesto Damiani
2023· article· en· Information Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Temporal Regularization in Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joëlle Pineau, Doina Precup
2018· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Diversity-Enriched Option-Critic
Anand Kamat, Doina Precup
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Hypernetworks for Zero-Shot Transfer in Reinforcement Learning
Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois R. Hogan, Gregory Dudek, David Meger
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Solving Common-Payoff Games with Approximate Policy Iteration
Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch +3 more
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Characterizing Markov Decision Processes
Bohdana Ratitch, Doina Precup
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations

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