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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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Automated Software Engineering
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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.

41 results · 1 filter active ·
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20032025
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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.
41 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 41 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 41 of 41 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.

afffundno abstractunlabeled
Prioritizing test cases with string distances
Yves Ledru, Alexandre Petrenko, Sergiy Boroday, Nadine Mandran
2011· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
125
citations
affno abstractunlabeled
Understanding machine learning software defect predictions
Geanderson E. dos Santos, Eduardo Figueiredo, Adriano Veloso, Markos Viggiato, Nívio Ziviani
2020· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
98
citations
affno abstractunlabeled
Automatic, high accuracy prediction of reopened bugs
Xin Xia, David Lo, Emad Shihab, Xinyu Wang, Bo Zhou
2014· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
73
citations
affno abstractunlabeled
Monitoring and diagnosing software requirements
Yiqiao Wang, Sheila A. McIlraith, Yijun Yu, John Mylopoulos
2008· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
affno abstractunlabeled
Differencing logical UML models
Zhenchang Xing, Eleni Stroulia
2007· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
afffundno abstractunlabeled
Agile Parsing in TXL
Thomas Dean, James R. Cordy, Andrew J. Malton, Kevin A. Schneider
2003· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
affno abstractunlabeled
Example-based model-transformation testing
Marouane Kessentini, Houari Sahraoui, Mounir Boukadoum
2011· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
afffundunlabeled
Self-admitted technical debt in R: detection and causes
Rishab Sharma, Ramin Shahbazi, Fatemeh H. Fard, Zadia Codabux, Melina Vidoni
2022· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
25
citations
affno abstractunlabeled
Introduction
Andrea De Lucia, James R. Cordy, John Mylopoulos
2003· article· en· Automated Software Engineering
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About