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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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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 9 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.

fundno affno abstractunlabeled
Advances in Model and Data Engineering in the Digitalization Era
Philippe Fournier‐Viger, Ahmed E. Hassan, Ladjel Bellatreche, Ahmed Awad, Abderrahim Ait Wakrime, Yassine Ouhammou +1 more
2022· book· en· Communications in computer and information science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Simplifying model transformation chains by rule composition
Márk Asztalos, Eugene Syriani, Manuel Wimmer, Marouane Kessentini
2010· article· en· Model Driven Engineering Languages and Systems· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Demystifying Domain Specific Languages
Abdelilah Kahlaoui, Alain Abran
2013· book-chapter· en· Advances in systems analysis, software engineering, and high performance computing book series· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Object-Oriented Reengineering
Serge Demeyer, Yann‐Gaël Guéhéneuc, Anne Keller, Christian F. J. Lange, Kim Mens, Adrian Kuhn +1 more
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Theme section on model-driven requirements engineering
Ana Moreira, Gunter Mussbacher, Jo�ão Araújo, Pablo Sánchez
2022· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Documentation Methods for Visual Languages
Alexandre Grings, Marcus Vinicius dos Santos
2011· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Contributor contact details
Liming Liu, Takehiko Watanabe, Mathias Heger, M. Horstmann, Gang Song, Zhao Dong Zhang +5 more
2010· book-chapter· en· Elsevier eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
affunlabeled
Visual scripting for handheld computers
Steven G. Armstrong, Y. Kollet, Trevor J. Smedley
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
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