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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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Journal of Software Evolution and Process
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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.

86 results · 1 filter active ·
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20102025
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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.
86 works in the cohort · of 4,299,418page 1 of 2

Labels cover 0 of 86 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 86 of 86 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.

affunlabeled
Contextualizing agile software development
Philippe Kruchten
2011· article· en· Journal of Software Evolution and Process· Computer Science
machine prediction:candidate · noneconsensus · none
152
citations
affunlabeled
On the evolution of Lehman's Laws
Michael W. Godfrey, Daniel M. Germán
2013· article· en· Journal of Software Evolution and Process· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
afffundunlabeled
Guidelines for evaluating bug‐assignment research
Ali Sajedi‐Badashian, Eleni Stroulia
2020· article· en· Journal of Software Evolution and Process· Computer Science
machine prediction:candidate · metaresearchconsensus · metaresearch
20
citations
affunlabeled
Towards reducing the time needed for load testing
Hammam M. AlGhamdi, Cor‐Paul Bezemer, Weiyi Shang, Ahmed E. Hassan, Parminder Flora
2020· article· en· Journal of Software Evolution and Process· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Database engines: Evolution of greenness
Andriy Miranskyy, Zainab Al-Zanbouri, D. Godwin, Ayşe Bener
2017· article· en· Journal of Software Evolution and Process· Engineering
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
SCAN: an approach to label and relate execution trace segments
Soumaya Medini, Venera Arnaoudova, Massimiliano Di Penta, Giuliano Antoniol, Yann‐Gaël Guéhéneuc, Paolo Tonella
2014· article· en· Journal of Software Evolution and Process· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
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