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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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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.

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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 52 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
em-SPADE
Sandeep Chaudhary, Sebastian Fischmeister, Lin Tan
2014· article· en· ACM SIGPLAN Notices· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Evolution of the writer's role
Luc Chamberland
2000· article· en· International Professional Communication Conference· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Analyzing a decade of Linux system calls
Mojtaba Bagherzadeh, Nafıseh Kahani, Cor‐Paul Bezemer, Ahmed E. Hassan, Juergen Dingel, James R. Cordy
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Refactoring clones: a new perspective
Nikolaos Tsantalis, Giri Panamoottil Krishnan
2013· article· en· International Workshop on Software Clones· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Software clustering by example
Martin Faunes, Marouane Kessentini, Houari Sahraoui
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Learning to Rank with BERT for Argument Quality Evaluation
Charles-Olivier Favreau, Amal Zouaq, Sameer Bhatnagar
2022· article· en· Proceedings of the ... International Florida Artificial Intelligence Research Society Conference· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Design Patterns as Laws of Quality
Yann‐Gaël Guéhéneuc, Jean-Yves Guyomarc’h, Khashayar Khosravi, Hourari Sahraoui
2011· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Clones and Macro-Co-Changes
Angela Lozano Rodriguez, Fehmi Jaafar, Kim Mens, Yann Gaël Guéhéneuc
2014· article· en· VUBIR (Vrije Universiteit Brussel)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Software diversity
John McHugh
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Realistic bug triaging
Ali Sajedi Badashian
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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