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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
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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 45 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
An Exploratory Study on Machine Learning Model Management
Jasmine Latendresse, Samuel Abedu, Ahmad Abdellatif, Emad Shihab
2024· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
venueno affunlabeled
Understanding and predicting bugs fixed by API-migrations
Nouh Alhindawi, Omar Meqdadi, Jamal Alsakran, Nader Mohammad Aljawarneh, Hatim S. Migdadi
2022· article· en· International Journal of Data and Network Science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
The programmer life-cycle
Russell Ovans
2004· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Identifying Concepts in Software Projects
Mathieu Nassif, Martin P. Robillard
2023· article· en· IEEE Transactions on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
Programming by Example Made Easy
Jiarong Wu, Lili Wei, Yanyan Jiang, Shing-Chi Cheung, Luyao Ren, Chang Xu
2023· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
On tabular expressions
Ryszard Janicki, Alan Wassyng
2003· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Does Requirements Clustering Lead to Modular Design?
Zude Li, Quazi Abidur Rahman, Remo Ferrari, Nazim H. Madhavji
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Software Artifact Metamodel
Marcos Silva, Toacy Oliveira, Ricardo Melo Bastos
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About