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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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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.

106 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
106 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 106 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 106 of 106 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.

afffundunlabeled
Requirements: The Key to Sustainability
Christoph Becker, Stefanie Betz, Ruzanna Chitchyan, Letícia Duboc, Steve Easterbrook, Birgit Penzenstadler +2 more
2015· article· en· IEEE Software· Engineering
machine prediction:candidate · noneconsensus · none
190
citations
affunlabeled
Agility and Architecture: Can They Coexist?
Pekka Abrahamsson, Muhammad Ali Babar, Philippe Kruchten
2010· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
173
citations
affunlabeled
Software Bots
Carlene Lebeuf, Margaret‐Anne Storey, Alexey Zagalsky
2017· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
158
citations
affunlabeled
How API Documentation Fails
Gias Uddin, Martin P. Robillard
2015· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
157
citations
affunlabeled
Cyclomatic Complexity
Christof Ebert, James Cain, Giuliano Antoniol, Steve Counsell, Phillip A. Laplante
2016· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
128
citations
affunlabeled
Conceptualizing Digital Twins
Romina Eramo, Francis Bordeleau, Benoît Combemale, Mark van den Brand, Manuel Wimmer, Andreas Wortmann
2021· article· en· IEEE Software· Engineering
machine prediction:candidate · noneconsensus · none
122
citations
affunlabeled
Software Engineering for Big Data Systems
Ian Gorton, Ayşe Bener, Audris Mockus
2016· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Emotional Requirements
David Callele, Eric Neufeld, Kevin A. Schneider
2008· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Release Stabilization on Linux and Chrome
Md Tajmilur Rahman, Peter C. Rigby
2015· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Advancing Software Engineering Professional Education
Mark A. Ardis, Pierre Bourque, Thomas B. Hilburn, Kahina Lasfer, Scott Lucero, James E. McDonald +2 more
2010· article· en· IEEE Software· Engineering
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Generative AI to Generate Test Data Generators
Benoît Baudry, Khashayar Etemadi, Sen Fang, Yogya Gamage, Yi Liu, Martin Monperrus +3 more
2024· preprint· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
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