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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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Model-Driven Software Engineering Techniques
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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
fundfunder
venuejournal
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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.

695 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.
695 works in the cohort · of 4,299,418page 4 of 14

Labels cover 3 of 695 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 695 of 695 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
JRefleX
Kenny Wong, Warren Blanchet, Ying Liu, Curtis Schofield, Eleni Stroulia, Zhenchang Xing
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
A TOOL BASED ON DL FOR UML MODEL CONSISTENCY CHECKING
Jocelyn Simmonds, María Cecilia Bastarrica, Nancy Hitschfeld, Sebastián Rivas
2008· article· en· International Journal of Software Engineering and Knowledge Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
affunlabeled
Partial evaluation of model transformations
Ali Razavi, Kostas Kontogiannis
2012· article· en· International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
venueno affunlabeled
MODEL-BASED DESIGN FOR SYSTEM INTEGRATION
Pieter J. Mostermanm, Jason Ghidella, Jon Friedman
2011· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affno abstractunlabeled
Models in Software Engineering
Juergen Dingel, Arnor Solberg
2011· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
MDebugger
Mojtaba Bagherzadeh, Nicolas Hili, David Seekatz, Juergen Dingel
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
System Analysis and Modeling
2005· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Current Practices in the Use of UML
Brian Dobing, Jeffrey Parsons
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Exceptional Transformations
Eugene Syriani, Jörg Kienzle, Hans Vangheluwe
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
A model driven approach for AMF configuration generation
Pejman Salehi, Pietro Colombo, Abdelwahab Hamou‐Lhadj, Ferhat Khendek
2010· article· en· System Analysis and Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
fundno affgemma · no categorygpt · no categorymodels split
DOMAIN-SPECIFIC MODELLING WITH ATOM
Hans Vangheluwe, Ximeng Sun, Eric Bodden
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
MU-MMINT: An IDE for Model Uncertainty
Michalis Famelis, Naama Ben-David, Alessio Di Sandro, Rick Salay, Marsha Chećhik
2015· article· en· 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Automating Activities in MDE Tools
Miguel Andrés Gamboa, Eugene Syriani
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About