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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 3 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
Automatic Summarization of Bug Reports
Sarah Rastkar, Gail C. Murphy, Gabriel Murray
2014· article· en· IEEE Transactions on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
182
citations
affunlabeled
Security versus performance bugs
Shahed Zaman, Bram Adams, Ahmed E. Hassan
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
180
citations
affno abstractunlabeled
What do developers search for on the web?
Xin Xia, Lingfeng Bao, David Lo, Pavneet Singh Kochhar, Ahmed E. Hassan, Zhenchang Xing
2017· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
177
citations
affunlabeled
Predicting Eclipse Bug Lifetimes
Lucas D. Panjer
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
176
citations
afffundunlabeled
Mining questions asked by web developers
Kartik Bajaj, Karthik Pattabiraman, Ali Mesbah
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
154
citations
affunlabeled
Fingerprinting design patterns
Gueheneuc Y-G, Houari Sahraoui, Fatiha Zaïdi
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
150
citations
affunlabeled
Communication, collaboration, and bugs
Dane Bertram, Amy Voida, Saul Greenberg, Robert J. Walker
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
147
citations
affunlabeled
Resource Estimation in Software Engineering
Lionel Briand, Isabella Wieczorek
2002· other· en· Encyclopedia of Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
146
citations
affunlabeled
An empirical study of build maintenance effort
Shane McIntosh, Bram Adams, Thanh Nguyen, Yasutaka Kamei, Ahmed E. Hassan
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
139
citations
affno abstractunlabeled
Studying re-opened bugs in open source software
Emad Shihab, Akinori Ihara, Yasutaka Kamei, Walid M. Ibrahim, Masao Ohira, Bram Adams +2 more
2012· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
138
citations
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