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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 51 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
Pattern-Oriented Use Case Modeling
Pankaj Kamthan
2009· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
A Survey on Code Representation
Peter D. Nagy, Marzieh Ahmadi Najafabadi, Heidar Davoudi
2023· article· en· World Scientific Annual Review of Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Software Security Engineering – Part I
Issa Traoré, Isaac Woungang
2013· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Program Slicing in the Era of Large Language Models
Kimya Khakzad Shahandashti, Mohammad Mahdi Mohajer, Alvine Boaye Belle, Song Wang, Hadi Hemmati Lassonde
2025· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Mining Action Rules for Defect Reduction Planning
Khouloud Oueslati, Gabriel Laberge, Maxime Lamothe, Foutse Khomh
2024· article· en· Proceedings of the ACM on software engineering.· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Report on MSR 2005
Stephan Diehl, Ahmed E. Hassan, Richard C. Holt
2005· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
affno abstractunlabeled
Novice Type Error Diagnosis with Natural Language Models
Chuqin Geng, Haolin Ye, Yixuan Li, Tianyu Han, Brigitte Pientka, Xujie Si
2022· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundno abstractunlabeled
Assessing the exposure of software changes
Mehran Meidani, Maxime Lamothe, Shane McIntosh
2023· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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