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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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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.

109 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.
109 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 109 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 109 of 109 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
Knowledge, action, and the frame problem
Richard B. Scherl, Hector J. Levesque
2003· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
276
citations
afffundno abstractunlabeled
The challenge of poker
Darse Billings, Aaron Davidson, Jonathan Schaeffer, Duane Szafron
2002· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
271
citations
affno abstractunlabeled
Games solved: Now and in the future
H.J. van den Herik, J.W.H.M. Uiterwijk, Jack van Rijswijck
2002· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
253
citations
affunlabeled
The Hanabi challenge: A new frontier for AI research
Nolan Bard, Jakob Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, Hai-Jing Song +9 more
2019· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
237
citations
afffundno abstractunlabeled
ASlib: A benchmark library for algorithm selection
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette +5 more
2016· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
213
citations
affno abstractunlabeled
Generating and evaluating evaluative arguments
Giuseppe Carenini, Johanna D. Moore
2006· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
191
citations
affno abstractunlabeled
Computer Go
Martin Müller
2002· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
180
citations
afffundno abstractunlabeled
Optimal social choice functions: A utilitarian view
Craig Boutilier, Ioannis Caragiannis, Simi Haber, Tyler Lu, Ariel D. Procaccia, Or Sheffet
2015· article· en· Artificial Intelligence· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
144
citations
affno abstractunlabeled
On our best behaviour
Hector J. Levesque
2014· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
103
citations
affno abstractunlabeled
Approximate inference in Boltzmann machines
Max Welling, Yee Whye Teh
2002· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
83
citations
affno abstractunlabeled
Learning heuristic functions for large state spaces
Shahab Jabbari Arfaee, Sandra Zilles, Robert C. Holte
2011· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
77
citations
affno abstractunlabeled
Iterated belief change in the situation calculus
Steven Shapiro, Maurice Pagnucco, Yves Lespérance, Hector J. Levesque
2010· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
75
citations
afffundno abstractunlabeled
Games, computers, and artificial intelligence
Jonathan Schaeffer, H.J. van den Herik
2002· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
74
citations
affno abstractunlabeled
Expressing preferences in default logic
James P. Delgrande, Torsten Schaub
2000· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
affno abstractunlabeled
A consistency-based approach for belief change
James P. Delgrande, Torsten Schaub
2003· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
64
citations
affno abstractunlabeled
Inductive situation calculus
Marc Denecker, Eugenia Ternovska
2007· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affno abstractunlabeled
Inconsistent heuristics in theory and practice
Ariel Felner, Uzi Zahavi, Robert C. Holte, Jonathan Schaeffer, Nathan Sturtevant, Zhifu Zhang
2011· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
59
citations
affno abstractunlabeled
Multi-agent learning for engineers
Shie Mannor, Jeff S. Shamma
2007· article· en· Artificial Intelligence· Decision Sciences
machine prediction:candidate · noneconsensus · none
52
citations
afffundno abstractunlabeled
Belief revision in Horn theories
James P. Delgrande, Pavlos Peppas
2014· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
fundno affno abstractunlabeled
Applying MDL to learn best model granularity
Qiong Gao, Ming Li, Paul Vitányi
2000· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
affno abstractunlabeled
Bounded situation calculus action theories
Giuseppe De Giacomo, Yves Lespérance, Fabio Patrizi
2016· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
35
citations

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