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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 1 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
How UML is used
Brian Dobing, Jeffrey Parsons
2006· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · metaresearchconsensus · none
380
citations
affno abstractunlabeled
Survey and classification of model transformation tools
Nafıseh Kahani, Mojtaba Bagherzadeh, James R. Cordy, Juergen Dingel, Dániel Varró
2018· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
126
citations
affunlabeled
Identifying Difficulties in Learning Uml
Keng Siau, Poi-Peng Loo
2006· article· en· Information Systems Management· Computer Science
machine prediction:candidate · noneconsensus · none
113
citations
affno abstractunlabeled
Clafer: unifying class and feature modeling
Kacper Bąk, Zinovy Diskin, Michał Antkiewicz, Krzysztof Czarnecki, Andrzej Wąsowski
2014· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
102
citations
affno abstractunlabeled
Model transformation intents and their properties
Lucio Levi, Moussa Amrani, Juergen Dingel, Leen Lambers, Rick Salay, Gehan Selim +2 more
2014· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
102
citations
affno abstractunlabeled
Model Transformation as an Optimization Problem
Marouane Kessentini, Houari Sahraoui, Mounir Boukadoum
2008· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
95
citations
affno abstractunlabeled
Search-based model transformation by example
Marouane Kessentini, Houari Sahraoui, Mounir Boukadoum, Omar Ben Omar
2010· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
affunlabeled
Lucx: lucid enriched with context
Joey Paquet, V. S. Alagar, Kaiyu Wan
2006· dissertation· en· Computer Science
machine prediction:candidate · noneconsensus · none
73
citations
affno abstractunlabeled
Opportunities in intelligent modeling assistance
Gunter Mussbacher, Benoît Combemale, Jörg Kienzle, Silvia Abrahão, Hyacinth Ali, Nelly Bencomo +11 more
2020· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
63
citations
affunlabeled
Domain-retargetable reverse engineering
S. Tilley, Hausi Müller, Michael J. Whitney, Kevin Wong
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
59
citations
affno abstractunlabeled
Global Grammar Constraints
Claude-Guy Quimper, Toby Walsh
2006· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
affunlabeled
Quick fix generation for DSMLs
Ábel Hegedüs, Ákos Horváth, István Ráth, Dániel Varró
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affno abstractunlabeled
Understanding and improving UML package merge
Jürgen Dingel, Zinovy Diskin, Alanna Zito
2007· article· en· Software & Systems Modeling· Computer Science
machine prediction:candidate · noneconsensus · none
48
citations
affno abstractunlabeled
Using Macromodels to Manage Collections of Related Models
Rick Salay, John Mylopoulos, Steve Easterbrook
2009· book-chapter· en· Notes on numerical fluid mechanics and multidisciplinary design· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affno abstractunlabeled
Transformation of Models Containing Uncertainty
Michalis Famelis, Rick Salay, Alessio Di Sandro, Marsha Chećhik
2013· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affno abstractunlabeled
Model-Driven Engineering Languages and Systems
Juergen Dingel, Wolfram Schulte, Isidro Ramos, Silvia Abrahão, Emilio Insfrán
2014· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
42
citations

How this was built: Screen · Findings · About