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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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Multi-Agent Systems and Negotiation
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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.

940 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.
940 works in the cohort · of 4,299,418page 5 of 19

Labels cover 0 of 940 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 940 of 940 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
A Carneades reconstruction of Popov v Hayashi
Thomas F. Gordon, Douglas Walton
2012· article· en· Artificial Intelligence and Law· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
Bringing Coherence to Agent Conversations
Roberto A. Flores, Robert C. Kremer
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Role-Based Multi-Agent Systems
Haibin Zhu, MengChu Zhou
2011· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
fundno affno abstractunlabeled
Automatic Argumentation Extraction
Alan Sergeant
2013· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Proleptic Argumentation
Douglas Walton
2008· article· en· Argumentation and Advocacy· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
A CBR-Based Approach for Ship Collision Avoidance
Yuhong Liu, Chunsheng Yang, Xuanmin Du
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Content Analysis Through the Machine Learning Mill
Vivi Năstase, Sabine T. Koeszegi, Stan Śzpakowicz
2006· article· en· Group Decision and Negotiation· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Engineering e-Collaboration Services with a Multi-Agent System Approach
Dickson K.W. Chiu, Shing-Chi Cheung, Ho-fung Leung, Patrick C. K. Hung, Eleanna Kafeza, Hua Hu +3 more
2010· article· en· International Journal of Systems and Service-Oriented Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations

How this was built: Screen · Findings · About