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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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Advanced Software Engineering Methodologies
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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.

1,299 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.
1,299 works in the cohort · of 4,299,418page 2 of 26

Labels cover 1 of 1,299 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 1,299 of 1,299 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
Model-driven software product lines
Krzysztof Czarnecki, Michał Antkiewicz, Chang Hwan Peter Kim, Sean Lau, Krzysztof Pietroszek
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
94
citations
affunlabeled
Adding trace matching with free variables to AspectJ
Chris Allan, Pavel Avgustinov, Aske Simon Christensen, Laurie Hendren, Sascha Kuzins, Ondřej Lhoták +4 more
2005· article· en· ACM SIGPLAN Notices· Computer Science
machine prediction:candidate · noneconsensus · none
91
citations
affunlabeled
Optimising aspectJ
Pavel Avgustinov, Aske Simon Christensen, Laurie Hendren, Sascha Kuzins, Jennifer Lhoták, Ondřej Lhoták +4 more
2005· article· en· ACM SIGPLAN Notices· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
affunlabeled
Towards requirements-driven autonomic systems design
Alexei Lapouchnian, Sotirios Liaskos, John Mylopoulos, Yijun Yu
2005· article· en· ACM SIGSOFT Software Engineering Notes· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Analyzing goal models
Jennifer Horkoff, Eric Yu
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
78
citations
affunlabeled
Advanced exception handling mechanisms
Peter A. Buhr, Wai-Meng Mok
2000· article· en· IEEE Transactions on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
76
citations
affunlabeled
Jedd
Ondřej Lhoták, Laurie Hendren
2004· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
76
citations
affunlabeled
Reverse engineering goal models from legacy code
Yijun Yu, Yiqiao Wang, John Mylopoulos, Sotirios Liaskos, Alexei Lapouchnian, Julio César Sampaio do Prado Leite
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
74
citations
afffundunlabeled
Lifting model transformations to product lines
Rick Salay, Michalis Famelis, Julia Rubin, Alessio Di Sandro, Marsha Chećhik
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
affno abstractunlabeled
Monitoring and diagnosing software requirements
Yiqiao Wang, Sheila A. McIlraith, Yijun Yu, John Mylopoulos
2008· article· en· Automated Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
affunlabeled
Refactoring middleware with aspects
C. Zhang, Hans‐Arno Jacobsen
2003· article· en· IEEE Transactions on Parallel and Distributed Systems· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
affunlabeled
Clafer tools for product line engineering
Michał Antkiewicz, Kacper Bąk, Alexandr Murashkin, Rafael Olaechea, Liang Jia, Krzysztof Czarnecki
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
affno abstractunlabeled
Towards Advanced Goal Model Analysis with jUCMNav
Daniel Amyot, Azalia Shamsaei, Jason Kealey, Etienne Tremblay, Andrew Miga, Gunter Mussbacher +4 more
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
71
citations
affno abstractunlabeled
Control Flow Analysis of UML 2.0 Sequence Diagrams
Vahid Garousi, Lionel Briand, Yvan Labiche
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
67
citations
affunlabeled
Visual variability analysis for goal models
B. Gonzales-Baixauli, Juarez Leite, John Mylopoulos
2004· article· en· Institutional Research Information System (Università degli Studi di Trento)· Computer Science
machine prediction:candidate · noneconsensus · none
63
citations
affunlabeled
A core ontology for requirements
Ivan Jureta, John Mylopoulos, Stéphane Faulkner
2009· article· en· Applied Ontology· Computer Science
machine prediction:candidate · noneconsensus · none
61
citations
affunlabeled
Software product lines: a case study
Mark A. Ardis, Nigel Daley, Daniel Hoffman, Harvey Siy, David M. Weiss
2000· article· en· Software Practice and Experience· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
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