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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
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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 53 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.

affno abstractunlabeled
Towards a Framework for Software Product Maturity Measurement
Mohammad Alshayeb, Ahmad Abdellatif, Sami Zahran, Mahmood Niazi
2015· article· en· International Conference on Software Engineering Advances· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
SourceVis
Craig Anslow, Stuart Marshall, James Noble, Robert Biddle
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Point/Counterpoint
Kurt Wallnau, Philippe Kruchten
2011· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
<scp>AntiCopyPaster</scp> 3.0: Just-in-Time Clone Refactoring
Eman Abdullah AlOmar, Jacob Ashkenas, Robert Feliciano, Matthew Angelakos, Dimitrios Haralamppopoulos, Xing Qian +2 more
2025· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Analyzing and Repairing Compilation Errors
Ali Mesbah, Andrew Rice, Edward Aftandilian, Emily Johnston, Nick Glorioso
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Towards Understanding Refactoring Engine Bugs
Haibo Wang, Huaien Zhang, Nikolaos Tsantalis, Shin Hwei Tan
2025· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Semantic-Enabled Clone Detection
Iman Keivanloo, Juergen Rilling
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Predictive Software Models
Jelber Sayyad Shirabad, Stan Matwin, Timothy C. Lethbridge
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
The Software Reliability Increase Method
Svetlana A. Yaremchuk, Dmitry Maevsky
2014· article· en· Studies in sociology of science· Computer Science
machine prediction:candidate · stsconsensus · none
2
citations
afffundunlabeled
Do Developers Refactor Data Access Code? An Empirical Study
Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol
2022· article· en· 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

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