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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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Visual and Cognitive Learning Processes
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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.

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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.

401 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.
401 works in the cohort · of 4,299,418page 6 of 9

Labels cover 0 of 401 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 401 of 401 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.

affno abstractunlabeled
Adaptive Tips for Helping Domain Experts
Alana Cordick, Judi McCuaig
2009· book-chapter· en· Lecture notes in computer science· Psychology
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Learning From the News
Heather O’Brien, Jocelyn McKay, Jacob Vangeest
2017· article· en· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
Statistics Commentary Series
David L. Streiner
2019· article· en· Journal of Clinical Psychopharmacology· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
As imagens no PowerPoint
Luc Desnoyers
2009· article· en· Laboreal· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Focused Learning
Thomas R. Robinson, Catherine M. Burns
2013· article· en· Proceedings of the Human Factors and Ergonomics Society Annual Meeting· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Aging and Online Learning
Patricia Boechler
2009· book-chapter· en· IGI Global eBooks· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Approaching text genre
Petar Milin, Filip Nenadić, Michael Ramscar
2020· article· en· Scientific Study of Literature· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Looking or Listening?
Sachi Mizobuchi, Mark Chignell, David Canella, Moshe Eizenman, Sayaka Yoshizu, Chihiro Sannomiya +1 more
2013· article· en· Proceedings of the Human Factors and Ergonomics Society Annual Meeting· Psychology
machine prediction:candidate · noneconsensus · none
1
citations
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