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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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AI-based Problem Solving and Planning
Retraction
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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.

618 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.
618 works in the cohort · of 4,299,418page 6 of 13

Labels cover 1 of 618 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 618 of 618 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
Boosting Search Guidance in Problems with Semantic Attachments
Sara Bernardini, Maria Fox, Derek Long, Chiara Piacentini
2017· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Game-SAT: A Preliminary Report.
Ling Zhao, Martin Müller
2004· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundno abstractunlabeled
Planning for a Mobile Robot to Attend a Conference
Éric Beaudry, Froduald Kabanza, François Michaud
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Selective Dyna-style Planning Under Limited Model Capacity
Muhammad Zaigham Zaheer, Samuel Sokota, Erin J. Talvitie, Martha White
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
LM-Cut Heuristics for Optimal Linear Numeric Planning
Ryo Kuroiwa, Alexander Shleyfman, J. Christopher Beck
2022· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
Exploiting N-Gram Analysis to Predict Operator Sequences
Christian Muise, Sheila A. McIlraith, Jorge A. Baier, Michael E. Reimer
2009· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Cognitive Tutoring System with "Consciousness"
Daniel M. Dubois, Mohamed Gaha, Roger Nkambou, Pierre Poirier
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
The Two-Edged Nature of Diverse Action Costs
Gaojian Fan, Martin Müller, Robert C. Holte
2017· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Heuristic subset selection in classical planning
Levi H. S. Lelis, Santiago Franco, Marvin Abisrror, Mike Barley, Sandra Zilles, Robert C. Holte
2016· article· en· ResearchSpace (University of Auckland)· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
Fast and Accurate Predictions of IDA*'s Performance
Levi H. S. Lelis, Sandra Zilles, Robert C. Holte
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
afffundunlabeled
The Complexity of Partial-Order Plan Viability Problems
Xing Tan, Michael Grüninger
2014· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
afffundunlabeled
Iterative Budgeted Exponential Search
Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan Sturtevant
2019· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Using event-streams for fault-management in MAS
Peng Xu, Ralph Deters
2004· article· en· IEEE/WIC/ACM International Conference on Intelligent Agent Technology· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Numeric Planning via Search Space Abstraction.
León Illanes, Sheila A. McIlraith
2016· article· en· International Joint Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
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