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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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Web Applications and Data Management
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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
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

130 results · 1 filter active ·
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20012025
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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.
130 works in the cohort · of 4,299,418page 1 of 3

Labels cover 1 of 130 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 130 of 130 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
Eclipse help system
Kari L. Halsted, J. H. Roberts
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
58
citations
affunlabeled
Collocation methods for third-kind VIEs
Sonia Seyed Allaei, Zhan-Wen Yang, Hermann Brunner
2016· article· en· IMA Journal of Numerical Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
35
citations
affunlabeled
A model for web-based course registration systems
Ruben Estevez, Sean Rankin, Ricardo Silva
2014· article· en· International Journal of Web Information Systems· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
afffundunlabeled
PENS: A Personalized Electronic News System
M. Nadjarbashi-Noghani, Kazemi Malihe Sadat, Ali A. Ghorbani
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Engineering Web-Based Systems with UML Assets
Grant Larsen, Jim Conallen
2002· article· en· Annals of Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Web Engineering: An Assessment of Empirical Research
Bouchaïb Bahli, Dany Di Tullio
2003· article· en· Communications of the Association for Information Systems· Computer Science
machine prediction:candidate · metaresearchconsensus · metaresearch
7
citations
affunlabeled
Design principles for educational software
Yael Kali, Nathan Bos, Marcia C. Linn, Jody S. Underwood, Jim Hewitt
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
venueno affno abstractunlabeled
Increasing usability for web engineering methods
Karzan Wakil, Dayang N. A. Jawawi
2017· article· en· Review of Computer Engineering Studies· Computer Science
machine prediction:candidate · metaresearchconsensus · none
4
citations
affno abstractunlabeled
Web Engineering
David Lowe, Martin Gaedke
2005· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Keeping up with web development trends
Craig S. Miller, Jack Zheng, Randy Connolly, Amos O. Olagunju
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
FTT
Ming Ying, James Miller
2011· article· en· International Journal of Systems and Service-Oriented Engineering· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
affunlabeled
Get Ready ... Get Set ... GO!
Riana Coetsee, Hesta Friedrich-Nel, Martina Gaisch, Somarie Holtzhausen, Ulrich D. Holzbaur, Deseré Kokt +9 more
2017· book· en· UJ Press eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
2
citations
affvenueunlabeled
Un métamodèle des graphes conceptuels
Olivier Gerbé, Guy W. Mineau, Rudolph K Keller
2007· article· fr· Revue d intelligence artificielle· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About