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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.

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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 11 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
Scoping Software Engineering for AI: The TSE Perspective
Sebastián Uchitel, Marsha Chećhik, Massimiliano Di Penta, Bram Adams, Nazareno Aguirre, Gabriele Bavota +33 more
2024· article· en· IEEE Transactions on Software Engineering· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
5
citations
affunlabeled
Value-driven Hindsight Modelling
Arthur Guez, Fabio Viola, Théophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup +2 more
2020· article· en· Neural Information Processing Systems· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
5
citations
affunlabeled
Planning with Expectation Models
Yi Wan, Zaheer Abbas, Adam White, Martha White, Richard S. Sutton
2019· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
5
citations
affunlabeled
On designing migrating agents
Kaveh Hassani, Won‐Sook Lee
2014· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
GA Based Task Allocation Models
Deo Prakash Vidyarthi, Biplab Kumer Sarker, Anil Kumar Tripathi, Laurence T. Yang
2008· book-chapter· en· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
4
citations
affunlabeled
Importance Resampling for Off-policy Prediction
Matthew Schlegel, Wesley Chung, Daniel Graves, Jian Qian, Martha White
2019· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
4
citations
affunlabeled
Multi-step linear Dyna-style planning
Hengshuai Yao, Shalabh Bhatnagar, Dongcui Diao
2009· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
affunlabeled
On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman
Chao Gao, Bilal Kartal, Pablo Hernández-Leal, Matthew E. Taylor
2019· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
4
citations
affno abstractunlabeled
Learning Agent Representations for Ice Hockey
Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan
2020· article· en· Neural Information Processing Systems· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
4
citations
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