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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
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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 4 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
What makes a popular academic AI repository?
2021· article· en· Empirical Software Engineering· Decision Sciences
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
17
citations
affno abstractunlabeled
Revisiting reopened bugs in open source software systems
Ankur Tagra, Haoxiang Zhang, Gopi Krishnan Rajbahadur, Ahmed E. Hassan
2022· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Studying logging practice in test code
Haonan Zhang, Yiming Tang, Maxime Lamothe, Heng Li, Weiyi Shang
2022· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Using code reviews to automatically configure static analysis tools
Fiorella Zampetti, Saghan Mudbhari, Venera Arnaoudova, Massimiliano Di Penta, Sebastiano Panichella, Giuliano Antoniol
2021· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
afffundno abstractunlabeled
An empirical study of Android Wear user complaints
Suhaib Mujahid, Giancarlo Sierra, Rabe Abdalkareem, Emad Shihab, Weiyi Shang
2018· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations

How this was built: Screen · Findings · About