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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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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.

159 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.
159 works in the cohort · of 4,299,418page 1 of 4

Labels cover 1 of 159 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 159 of 159 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundno abstractunlabeled
GitHub Copilot AI pair programmer: Asset or Liability?
Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, Zhen Ming Jiang
2023· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · noneconsensus · none
340
citations
affaboutno abstractunlabeled
A survey of software testing practices in Canada
Vahid Garousi, Junji Zhi
2013· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · metaresearchconsensus · none
168
citations
afffundno abstractunlabeled
Automated impact analysis of UML models
Lionel Briand, Yvan Labiche, Lilian O’Sullivan, Michal M. Sówka
2005· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · noneconsensus · none
119
citations
affno abstractunlabeled
What do software architects really do?
Philippe Kruchten
2008· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · noneconsensus · none
107
citations
affno abstractunlabeled
A survey of self-admitted technical debt
Giancarlo Sierra, Emad Shihab, Yasutaka Kamei
2019· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · noneconsensus · none
78
citations
affno abstractunlabeled
Mining trends and patterns of software vulnerabilities
Syed Shariyar Murtaza, Wael Khreich, Abdelwahab Hamou‐Lhadj, Ayşe Bener
2016· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · noneconsensus · none
51
citations
affno abstractunlabeled
Development and validation of customized process models
Mohsen Asadi, Bardia Mohabbati, Gerd Gröner, Dragan Gašević
2014· article· en· Journal of Systems and Software· Business, Management and Accounting
distilled prediction:candidate · noneconsensus · none
26
citations
affno abstractunlabeled
Topic-based software defect explanation
Tse-Hsun Chen, Weiyi Shang, Meiyappan Nagappan, Ahmed E. Hassan, Stephen W. Thomas
2016· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · noneconsensus · none
24
citations
afffundno abstractunlabeled
An empirical study on bug propagation through code cloning
Manishankar Mondal, Banani Roy, Chanchal K. Roy, Kevin A. Schneider
2019· article· en· Journal of Systems and Software· Computer Science
distilled prediction:candidate · noneconsensus · none
22
citations

How this was built: Screen · Findings · About