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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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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.

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 6 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.

afffundunlabeled
ConcernMapper
Martin P. Robillard, Frédéric Weigand-Warr
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
89
citations
affunlabeled
High-impact defects
Emad Shihab, Audris Mockus, Yasutaka Kamei, Bram Adams, Ahmed E. Hassan
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
afffundunlabeled
Dimensions of UML Diagram Use
Brian Dobing, Jeffrey Parsons
2008· article· en· Journal of Database Management· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
affunlabeled
Systematizing pragmatic software reuse
Reid Holmes, Robert J. Walker
2012· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
affunlabeled
Software cost estimation with fuzzy models
Petr Musı́lek, Witold Pedrycz, Giancarlo Succi, Marek Reformat
2000· article· en· ACM SIGAPP Applied Computing Review· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
affunlabeled
CoRReCT
Mohammad Masudur Rahman, Chanchal K. Roy, Jason A. Collins
2016· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
87
citations
affunlabeled
Assessing the Refactorability of Software Clones
Nikolaos Tsantalis, Davood Mazinanian, Giri Panamoottil Krishnan
2015· article· en· IEEE Transactions on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
86
citations
affunlabeled
Recovering binary class relationships
Yann‐Gaël Guéhéneuc, Hervé Albin-Amiot
2004· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
85
citations
affunlabeled
Technical debt
Philippe Kruchten, Robert L. Nord, İpek Özkaya, Davide Falessi
2013· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Strathcona example recommendation tool
Reid Holmes, Robert J. Walker, Gail C. Murphy
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Predicting Bugs Using Antipatterns
Seyyed Ehsan Salamati Taba, Foutse Khomh, Ying Zou, Ahmed E. Hassan, Meiyappan Nagappan
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
82
citations

How this was built: Screen · Findings · About