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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.

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 42 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Measuring subversions
Julius Davies
2011· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Duration Estimation Models for Open Source Software Projects
Donatien Koulla Moulla, Alain Abran, Kolyang
2021· article· en· International Journal of Information Technology and Computer Science· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
5
citations
affunlabeled
An empirical study on change recommendation
Manishankar Mondal, Chanchal K. Roy, Kevin A. Schneider
2015· article· en· Computer Science and Software Engineering· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Teaching software product lines
Mathieu Acher, Roberto E. Lopez-Herrejon, Rick Rabiser
2018· preprint· en· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
5
citations
affunlabeled
Feature location based on impact analysis
Abhishek Rohatgi, Abdelwahab Hamou‐Lhadj, Juergen Rilling
2007· article· en· International Conference on Software Engineering· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affunlabeled
A fine-grained data set and analysis of tangling in bug fixing commits
Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher A. Ghaleb, Kuljit Kaur Chahal +40 more
2022· preprint· en· Empirical Software Engineering· Computer Science
distilled prediction:candidate · metaepi_narrow+open_scienceconsensus · none
5
citations
affunlabeled
AUTOMATED THREAT IDENTIFICATION FOR UML
George Yee, Xingli Xie, Shikharesh Majumdar
2010· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
afffundunlabeled
Does this apply to me?
Akalanka Galappaththi, Sarah Nadi, Christoph Treude
2022· preprint· en· Computer Science
distilled prediction:candidate · open_science+insufficient_payloadconsensus · none
5
citations
affunlabeled
Multi-language Design Smells
Mouna Abidi, Moses Openja, Foutse Khomh
2020· article· en· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
5
citations
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How this was built: Screen · Findings · About