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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 8 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
Proprietary versus Open Instruction Sets
Dave Christie, David A. Patterson, Joshua J. Yi, Derek Chiou, Resit Sendag
2016· article· en· IEEE Micro· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Improving Model Quality Using Diagram Coverage Criteria
Rick Salay, John Mylopoulos
2009· book-chapter· en· Notes on numerical fluid mechanics and multidisciplinary design· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Using OpenCL to Increase SCA Application Portability
Steve Bernier, François Lévesque, Martin Phisel, Dmitry Zvernik, David Hagood
2017· article· en· Journal of Signal Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
Long-form evaluation of model editing
Domenic Rosati, Robie Gonzales, Jinkun Chen, Xuemin Yu, Yahya Kayani, Frank Rudzicz +1 more
2024· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Heaven or Hell? A “Real-Time” UML?
Bran Selić, Alan Burns, Alan Moore, Theo Tempelmeier, François Terrier
2000· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
The model role level: a vision
Rick Salay, John Mylopoulos
2010· article· en· International Conference on Conceptual Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Recent Advances in Multi-Paradigm Modeling
Vasco Amaral, Cécile Hardebolle, Hans Vangheluwe, Peter Bunus
2024· article· en· Technische Universität Berlin – Universitätsbibliothek· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
The Model Role Level – A Vision
Rick Salay, John Mylopoulos
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
PolyDebug: A Framework for Polyglot Debugging
Philémon Houdaille, Djamel Eddine Khelladi, Benoit Combemale, Gunter Mussbacher, Tijs van der Storm
2025· article· en· The Art Science and Engineering of Programming· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Generic Graphical Navigation for Modelling Tools
Hyacinth Ali, Gunter Mussbacher, Jörg Kienzle
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Model-Based Development
Juan de Lara, Esther Guerra, Hans Vangheluwe
2006· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Polyglot Software Development: Wait, What?
Gunter Mussbacher, Benoît Combemale, Jörg Kienzle, Loli Burgueño, Antonio García‐Domínguez, Jean‐Marc Jezéquél +7 more
2024· article· en· IEEE Software· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About