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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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Software Testing Verification and Reliability
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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
fundfunder
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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.

28 results · 1 filter active ·
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20002023
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
28 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 28 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 28 of 28 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.

affunlabeled
On reducing test length for FSMs with extra states
Adenilso Simão, Alexandre Petrenko, Nina Yevtushenko
2011· article· en· Software Testing Verification and Reliability· Computer Science
machine prediction:candidate · noneconsensus · none
38
citations
afffundunlabeled
State generation and automated class testing
Thomas Ball, Daniel Hoffman, Frank Ruskey, Richard L. Webber, Lee White
2000· article· en· Software Testing Verification and Reliability· Computer Science
machine prediction:candidate · noneconsensus · none
36
citations
afffundunlabeled
BUGSJS: a benchmark and taxonomy of JavaScript bugs
Péter Gyimesi, Béla Vancsics, Andrea Stocco, Davood Mazinanian, Árpád Beszédes, Rudolf Ferenć +1 more
2020· article· en· Software Testing Verification and Reliability· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
afffundunlabeled
Automatic fault localization for client‐side JavaScript
Frolin S. Ocariza, Guanpeng Li, Karthik Pattabiraman, Ali Mesbah
2015· article· en· Software Testing Verification and Reliability· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Eight maxims for software inspectors
Diane Kelly, Terry Shepard
2004· article· en· Software Testing Verification and Reliability· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
An approach for testing pointcut descriptors in AspectJ
Romain Delamare, Benoît Baudry, Sudipto Ghosh, Shashank Gupta, Yves Le Traon
2011· article· en· Software Testing Verification and Reliability· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
aboutno affunlabeled
Status and Awards
Jeff Offutt
2012· article· en· Software Testing Verification and Reliability· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About