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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 166 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.

fundno affno abstractunlabeled
Information and Communications Security
Ding Wang, Moti Yung, Zheli Liu, Xiaofeng Chen
2023· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Learning Classifier Systems
Martin V. Butz
2010· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Understanding IP Traffic Via Cluster Processes
Ian W. C. Lee, Abraham O. Fapojuwo
2007· book-chapter· en· Lecture notes in computer science· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Portability of Optimizations from SC to TSO
Akshay Gopalakrishnan, Clark Verbrugge
2025· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
“Value” Emerges from Imperfect Memory
Jorge Ramírez‐Ruiz, R. Becket Ebitz
2024· book-chapter· en· Lecture notes in computer science· Neuroscience
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Real-Time Validation of Retail Gasoline Prices
Mondelle Simeon, Howard J. Hamilton
2017· book-chapter· en· Lecture notes in computer science· Business, Management and Accounting
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Emergent Individual Factors for AR Education and Training
Brendan Kelley, Anil Ufuk Batmaz, Michael Humphrey, Cyane Tornatzky, Rosa Mikeal Martey, Francisco R. Ortega
2023· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Matching Uncertain Identities Against Sparse Knowledge
Steven Horn, Anthony W. Isenor, Moira MacNeil, Adrienne Turnbull
2015· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
MeSH Represented MEDLINE Query Results
Pif Edwards, Vlado Kešelj
2010· book-chapter· en· Lecture notes in computer science· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · metaresearchconsensus · none
1
citations
affno abstractunlabeled
Computing Clipped Products
Arthur C. Norman, Stephen M. Watt
2024· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Selective Sampling for Classification
François Laviolette, Mario Marchand, Sara Shanian
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
The First Simple Symmetric 11-Venn Diagram
Khalegh Mamakani, Frank Ruskey
2013· book-chapter· en· Lecture notes in computer science· Mathematics
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
A Note on Boosting Algorithms for Image Denoising
Cory Falconer, C. Sean Bohun, Mehran Ebrahimi
2017· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Distributed XML Processing
M. TAMER ÖZSU
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Appraisal of Emotions from Resources
Yathirajan Brammadesam Manavalan, Vadim Bulitko
2014· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

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