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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.

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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 107 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
Automatic Generation of Search Engines
Markian Hlynka, Jonathan Schaeffer
2006· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
RFCT: An Association-Based Causality Miner
Kamran Karimi, Howard J. Hamilton
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Counting Subgraphs in Relational Event Graphs
Farah Chanchary, Anil Maheshwari
2016· book-chapter· en· Lecture notes in computer science· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Drawing Planar Graphs with Many Collinear Vertices
Giordano Da Lozzo, Vida Dujmović, Fabrizio Frati, Tamara Mchedlidze, Vincenzo Roselli
2016· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
RTFX: On-Set Previs with UnrealEngine3
Lesley Northam, Joe Istead, Craig S. Kaplan
2011· book-chapter· en· Lecture notes in computer science· Engineering
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Information Capture Devices for Social Environments
Meghan Deutscher, Phillip Jeffrey, Nelson Siu
2004· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Computational Science and Its Applications - ICCSA 2006
Marina L. Gavrilova, David Taniar, Youngsong Mun, Antonio Laganà, Vipin Kumar, Osvaldo Gervasi +1 more
2006· book· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Energy-Optimal Broadcast in a Tree with Mobile Agents
Jerzy Czyzowicz, Krzysztof Diks, Jean Moussi, Wojciech Rytter
2017· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
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How this was built: Screen · Findings · About