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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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Experimental Learning in Engineering
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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.

892 results · 1 filter active ·
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20002025
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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.
892 works in the cohort · of 4,299,418page 6 of 18

Labels cover 1 of 892 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 892 of 892 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 affunlabeled
Play-Doh and Pendulums: Making Mass Moment of Inertia Fun
Kathleen A. Bieryla, Nikolene Schulz, Rebecca Levison, Heather Dillon
2020· article· en· 2020 ASEE Virtual Annual Conference Content Access Proceedings· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Mathematics in Engineering: The professors' vision
Gisela Hernandes Gomes, Alejandro S. González-Martín
2015· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
fundno affunlabeled
Teaching Physics with Computers
Robert Botet, Emmanuel Trizac
2005· article· en· European Journal of Physics· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
aboutno affunlabeled
Focus on software
Lawrence G. Rubin
2008· article· en· Physics Today· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Computer and Communication Engineering
Filippo Neri, Ke-Lin Du, A. A. San Blas, Zhiyu Jiang
2023· book· en· Communications in computer and information science· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Teaching future professors how to teach
Paul L. Bishop, Ting‐Fai Yu, Margaret J. Kupferle, Deborah M. Moll, Cristina Alonso‐Tristán, M. Koechling
2001· article· en· Water Science & Technology· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Helping Students to Feel Mechanics
Ryan Barrage, G. Wayne Brodland, Rania Al-Hammoud
2018· article· en· Engineering
machine prediction:candidate · noneconsensus · none
3
citations
venueno affunlabeled
Do Engineering – Anywhere, Anytime
Mark Walters, Erik Luther, Julia Dinolfo
2012· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affvenueunlabeled
Basic Electricity Courses Revisited
Khaled Arfa, G. Olivier, G.-E. April
2010· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
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