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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.

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.

2,372 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.
2,372 works in the cohort · of 4,299,418page 25 of 48

Labels cover 1 of 2,372 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 2,372 of 2,372 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
Matrices, Statistics and Big Data
Francisco Carvalho, Simo Puntanen
2019· book· en· Contributions to statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Gear Crack Assessment Using Correlation Dimension
Zhi Peng Feng, Ming J. Zuo, Fu Lei Chu, Cheng Xiao
2010· article· en· Applied Mechanics and Materials· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
New Books
2006· article· en· Physics Today· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Deep Learning
William W. Hsieh
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Unsupervised Learning
William W. Hsieh
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Learning the Structure of a Mathematical Group
Anna Jamrozki, Thomas R. Shultz
2007· article· en· eScholarship (California Digital Library)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Multirate Digital Signal Processing
D. Sundararajan
2024· book-chapter· en· Digital Signal Processing· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Tree-Based Classification and Regression
John H. Maindonald, W. John Braun, Jeffrey L. Andrews
2024· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Regression Models for Cyclic Data
Graham Upton, Kenneth A. Hickey, Aaron Stallard
2003· article· en· Journal of the Royal Statistical Society Series C (Applied Statistics)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
FPGA-Based Architectures for Random Forest Acceleration
Parisa Abdolrahim Poorheravi, Vincent Gaudet
2022· article· en· 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About