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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 Engineering Research
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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
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

3,468 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.
3,468 works in the cohort · of 4,299,418page 49 of 70

Labels cover 10 of 3,468 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 3,468 of 3,468 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
Leveraging Reviewer Experience in Code Review Comment Generation
Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Michael W. Godfrey, Chunhua Liu, Wachiraphan Charoenwet
2025· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
venueno affno abstractunlabeled
Managing Software Reuse with Perforce
Jo Woodison, Mandarin Consulting
2003· article· en· Canadian journal of medical technology· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundno abstractunlabeled
Preserving knowledge in software projects
Omar Alam, Bram Adams, Ahmed E. Hassan
2012· article· en· Journal of Systems and Software· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Applying data mining to software maintenance records
Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin
2003· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About