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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
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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
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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 7 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
Behavioural model fusion
Shiva Nejati, Marsha Chećhik
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Umple
Timothy C. Lethbridge, Abdulaziz Algablan
2020· book-chapter· en· Advances in systems analysis, software engineering, and high performance computing book series· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Mirador
Stephen C. Barrett, Greg Butler, Patrice Chalin
2010· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
5
citations
affunlabeled
Calur: an Action Language for UML-RT
Nicolas Hili, Ernesto Posse, Juergen Dingel
2018· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Model-Driven Architecture for Web Applications
Mohamed Taleb, Ahmed Seffah, Alain Abran
2007· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Rapid Development of Scoped User Interfaces
Denis Dubé, Jacob G. Beard, Hans Vangheluwe
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
PhyDSLK: a model-driven framework for generating exergames
María Teresa Baldassarre, Danilo Caivano, Simone Romano, Francesco Cagnetta, Víctor Fernández-Cervantes, Eleni Stroulia
2021· article· en· Multimedia Tools and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Component-based Modeling in Umple
Mahmoud Husseini Orabi, Ahmed Husseini Orabi, Timothy C. Lethbridge
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Lossless compaction of model execution traces
Fazilat Hojaji, Bahman Zamani, Abdelwahab Hamou‐Lhadj, Tanja Mayerhofer, Erwan Bousse
2019· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
BON development tool
Ali Taleghani, Jonathan S. Ostroff
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Umple as a Template Language (Umple-TL)
Mahmoud Husseini Orabi, Ahmed Husseini Orabi, Timothy C. Lethbridge
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
A Field Study of Modellers at Work
Eirini Kalliamvakou, Marc Palyart, Gail C. Murphy, Daniela Damian
2015· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
How do we teach Modelling and Model-Driven Engineering? A survey
Federico Ciccozzi, Michalis Famelis, Gerti Kappel, Leen Lambers, Sébastien Mosser, Richard F. Paige +6 more
2018· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations

How this was built: Screen · Findings · About