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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 196 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
Workshop on Java for Parallel and Distributed Computing
Denis Caromel, Serge Chaumette, Geoffrey Fox, Peter Graham
2000· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
The Polygon Burning Problem
William Evans, Rebecca Lin
2022· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
To Filter Prune, or to Layer Prune, That Is the Question
Sara Elkerdawy, Mostafa Elhoushi, Abhineet Singh, Hong Zhang, Nilanjan Ray
2021· preprint· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Learning from Average Experience
James Bergin, Dan Bernhardt
2003· book-chapter· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Compressing 2-D Shapes Using Concavity Trees
O. El Badawy, Mohamed S. Kamel
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Artificial Intelligence and Soft Computing
Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Żurada
2023· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
State Generation in the PARMC Model Checker
Owen Kaser
2001· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Information Distance and Applications
Ming Li
2007· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About