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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.

11,332 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.
11,332 works in the cohort · of 4,299,418page 115 of 227

Labels cover 8 of 11,332 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 11,332 of 11,332 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
Evaluation of General Set Expressions
Ehsan Chiniforooshan, Arash Farzan, Mehdi Mirzazadeh
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Task Model Simulation Using Interaction Templates
David Paquette, Kevin A. Schneider
2006· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Sparse Learning Based Linear Coherent Bi-clustering
Yi Shi, Xiaoping Liao, Xinhua Zhang, Guohui Lin, Dale Schuurmans
2012· book-chapter· en· Lecture notes in computer science· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Does Requirements Clustering Lead to Modular Design?
Zude Li, Quazi Abidur Rahman, Remo Ferrari, Nazim H. Madhavji
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
A First-Order Calculus for Allegories
Bahar Aameri, Michael Winter
2011· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Improving Communication Using 3D Animation
Laurent Ruhlmann, Benoı̂t Ozell, Michel Gagnon, Steve Bourgoin, Éric Charton
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Deep Learning Methods for Query Auto Completion
Manish Gupta, Meghana Joshi, Puneet Agrawal
2023· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Verification of CERT Secure Coding Rules: Case Studies
Syrine Tlili, Xiaochun Yang, Rachid Hadjidj, Mourad Debbabi
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Intensional High Performance Computing
Pierre Kuonen, Gilbert Babin, Nabil Abdennadher, Paul-Jean Cagnard
2000· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About