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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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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.

31 results · 1 filter active ·
Results by year
20042019
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Categories
Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
31 works in the cohort · of 4,299,418page 1 of 1

Labels cover 1 of 31 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 31 of 31 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
Applying evidence to your everyday practice
Denise Koufogiannakis, Ellen Crumley
2004· book-chapter· en· Facet eBooks· Health Professions
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
Recordkeeping Cultures
Gillian Oliver, Fiorella Foscarini
2019· book· en· Facet eBooks· Arts and Humanities
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
The library's place in a mobile space
Graham McCarthy, Sally Wilson
2018· book-chapter· en· Facet eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Citizen Engagement
Fiorella Foscarini
2018· book-chapter· en· Facet eBooks· Arts and Humanities
machine prediction:candidate · noneconsensus · none
1
citations
affaboutunlabeled
Editors’ introduction
Fiorella Foscarini, Heather MacNeil, Bonnie Mak, Gillian Oliver
2018· book-chapter· en· Facet eBooks· Arts and Humanities
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Conclusion
Mohamed Ally
2018· book-chapter· en· Facet eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affaboutunlabeled
Engaging with social media
Ken Laing, Hillary S. Webb
2018· book-chapter· en· Facet eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Conclusion
Mohamed Ally
2018· book-chapter· en· Facet eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Agree
Denise Koufogiannakis, Alison Brettle
2018· book-chapter· en· Facet eBooks· Health Professions
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Supporting research
Chris Mavergames
2018· book-chapter· en· Facet eBooks· Social Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Introduction
Mohamed Ally
2018· book-chapter· en· Facet eBooks· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Social tagging and public policy
Ryan Deschamps
2018· book-chapter· en· Facet eBooks· Social Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About