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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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Empirical Software Engineering
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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.

371 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
371 works in the cohort · of 4,299,418page 3 of 8

Labels cover 1 of 371 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 371 of 371 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
A practical approach to testing GUI systems
Ping Li, Toan Huynh, Marek Reformat, James Miller
2006· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
35
citations
affno abstractunlabeled
Do topics make sense to managers and developers?
Abram Hindle, Christian Bird, Thomas Zimmermann, Nachiappan Nagappan
2014· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affno abstractunlabeled
Management of community contributions
Nicolas Bettenburg, Ahmed E. Hassan, Bram Adams, Daniel M. Germán
2013· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · stsconsensus · none
34
citations
affno abstractunlabeled
Automated topic naming
Abram Hindle, Neil Ernst, Michael W. Godfrey, John Mylopoulos
2012· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affno abstractunlabeled
Analyzing a decade of Linux system calls
Mojtaba Bagherzadeh, Nafıseh Kahani, Cor‐Paul Bezemer, Ahmed E. Hassan, Juergen Dingel, James R. Cordy
2017· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
31
citations
affno abstractunlabeled
Bug characterization in machine learning-based systems
Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon, Foutse Khomh, Zhen Ming Jiang
2023· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
afffundno abstractunlabeled
SSPCatcher: Learning to catch security patches
Arthur D. Sawadogo, Tegawendé F. Bissyandé, Naouel Moha, Kevin Allix, Jacques Klein, Li Li +1 more
2022· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
affunlabeled
Software product-line evaluation in the large
Robert Lindohf, Jacob Krüger, Erik D. Herzog, Thorsten Berger
2021· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
afffundno abstractunlabeled
Empirical study of android repackaged applications
Kobra Khanmohammadi, Neda Ebrahimi, Abdelwahab Hamou‐Lhadj, Raphaël Khoury
2019· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations

How this was built: Screen · Findings · About