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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
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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
Evidence
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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 5 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.

afffundunlabeled
Computing Robust Plans in Continuous Domains
Christian Fritz, Sheila A. McIlraith
2009· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Improving Local Search for Resource-Constrained Planning
Hootan Nakhost, Jörg Hoffmann, Martin Müller
2010· article· en· Proceedings of the International Symposium on Combinatorial Search· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Model checking approach to automated planning
Yi Li, Jin Song Dong, Jing Sun, Yang Liu, Jun Sun
2013· article· en· Formal Methods in System Design· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
afffundunlabeled
Understanding and Improving Local Exploration for GBFS
Fan Xie, Martin Müller, Robert C. Holte
2015· article· en· Proceedings of the International Conference on Automated Planning and Scheduling· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Case Authoring from Text and Historical Experiences
Marvin Zaluski, Nathalie Japkowicz, Stan Matwin
2003· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
afffundno abstractunlabeled
A Constraint-Based Robotic Soccer Team
Yu Zhang, Alan K. Mackworth
2002· article· en· Constraints· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Are We There Yet? — Estimating Search Progress
Jordan Thayer, Roni Stern, Levi H. S. Lelis
2021· article· en· Proceedings of the International Symposium on Combinatorial Search· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Authoring Cases from Free-Text Maintenance Data
Chunsheng Yang, Robert Orchard, Benoit Farley, Maivin Zaluski
2007· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
venueno affno abstractunlabeled
Getting parameters from learning data
Denis Cousineau, Guy Lacroix
2006· article· en· Tutorials in Quantitative Methods for Psychology· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Control Network Programming
Kostadin Kratchanov, Tzanko Golemanov, Emilia Golemanova
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
fundno affunlabeled
A Spectrum of Plan Justifications
Eugene Fink, Qiang Yang
2018· article· en· Figshare· Computer Science
machine prediction:candidate · noneconsensus · none
7
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
afffundunlabeled
Cost-Based Query Optimization via AI Planning
Nathan Robinson, Sheila A. McIlraith, David Toman
2014· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
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
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
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