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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.

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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 55 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.

fundno affunlabeled
An exploratory study of the impact of software changeability
Foutse Khomh, Massimiliano Di Penta, Yann‐Gaël Guéhéneuc, Giuliano Antoniol
2009· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Impacts and detection of design smells
Esma Aı̈meur, Yann‐Gaël Guéhéneuc, Abdou Maïga
2012· dissertation· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Detecting and Correcting Typing Errors in DBpedia.
Daniel D. Caminhas, Daniel Cones, Natalie Hervieux, Denilson Barbosa
2019· article· en· Knowledge Discovery and Data Mining· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
How Is Software Reuse Discussed in Stack Overflow?
Eman Abdullah AlOmar, Anthony Peruma, Mohamed Wiem Mkaouer, Christian D. Newman, Ali Ouni
2024· book-chapter· en· Conference on systems engineering research series· Computer Science
machine prediction:candidate · metaresearchconsensus · none
1
citations
afffundunlabeled
"Bloat"
Joanna McGrenere
2000· article· en· CHI '00 extended abstracts on Human factors in computer systems - CHI '00· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Learning how to program
M. Afzal Upal, Srinivas Padmanabhuni
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
An Optimal Spacing Approach for Sampling Small-sized Datasets
Samuel Abedu, Solomon Mensah, Frederick Boafo, Eva Bushel, Elizabeth Akuafum
2023· article· en· Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Composing knowledge fragments
Thomas Fritz
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Understanding peer review of software engineering papers
Neil Ernst, Jeffrey C. Carver, Daniel Méndez, Marco Torchiano
2021· preprint· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · metaresearchconsensus · none
1
citations
affno abstractunlabeled
Special issue on program comprehension
Michael W. Godfrey, Arie van Deursen
2014· article· en· Empirical Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Ranking co-change candidates of micro-clones.
Manishankar Mondal, Banani Roy, Chanchal K. Roy, Kevin A. Schneider
2019· article· en· Conference of the Centre for Advanced Studies on Collaborative Research· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Data in DevOps and Its Importance in Code Analytics
G.Harinadh Babu, Charitra Kamalaksh Patil
2020· book-chapter· en· Advances in systems analysis, software engineering, and high performance computing book series· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
An empirical study of goto in C code
Meiyappan Nagappan, Romain Robbes, Yasutaka Kamei, Éric Tanter, Shane McIntosh, Audris Mockus +1 more
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About