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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.

1,099 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.
1,099 works in the cohort · of 4,299,418page 12 of 22

Labels cover 1 of 1,099 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 1,099 of 1,099 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.

venueno affno abstractunlabeled
Performance Testing from the Cloud
Tom Lounibos
2010· article· en· ˜The œopen source business resource· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Managing and Configuring On-Board Diagnostics
David A. Parenti
2009· article· en· SAE technical papers on CD-ROM/SAE technical paper series· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
MemRed
Masoomeh Rudafshani, Paul A. S. Ward, Bernard Wong
2012· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Junit framework for unit testing
Praveen Kumar Venkatesan, Rikhil Gade Rozario, Jinan Fiaidhi
2020· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
The Official Guide to DB2 Version 8
Zikopoulos, Roman B. Melnyk, George Baklarz
2003· book· en· Prentice Hall Professional Technical Reference eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
affunlabeled
The API Performance Contract
Robert F. Sproull, Jim Waldo
2014· article· en· Queue· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Statistical Anomaly Detection for Train Fleets
Anders Holst, Markus Bohlin, Jan Ekman, Ola Sellin, Björn Lindström, Stefan Larsen
2012· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
DEVS for AUTOSAR platform modelling
Joachim Denil, Hans Vangheluwe, Pieter Ramaekers, Paul De Meulenaere, Serge Demeyer
2011· article· en· Spring Simulation Multiconference· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Especificación de requisitos de un sistema IoT con UML
Daniel Laguía, Karim Hallar, Osiris Sofía, Leonardo González, Esteban Gesto
2022· article· es· Informes Científicos - Técnicos UNPA· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
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