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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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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.

2,769 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.
2,769 works in the cohort · of 4,299,418page 4 of 56

Labels cover 6 of 2,769 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 2,769 of 2,769 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
Pattern-based approaches to semantic relation extraction
Alain Auger, Caroline Barrière
2008· article· en· Terminology International Journal of Theoretical and Applied Issues in Specialized Communication· Computer Science
machine prediction:candidate · noneconsensus · none
92
citations
affno abstractunlabeled
Using context to build semantics
Peter J. Kwantes
2005· article· en· Psychonomic Bulletin & Review· Computer Science
machine prediction:candidate · noneconsensus · none
89
citations
affunlabeled
Language GANs Falling Short
M. Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joëlle Pineau, Laurent Charlin
2020· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
89
citations
affunlabeled
Towards Topic-to-Question Generation
Yllias Chali, Sadid A. Hasan
2015· article· en· Computational Linguistics· Computer Science
machine prediction:candidate · noneconsensus · none
82
citations
affunlabeled
Distraction-based neural networks for modeling documents
Chen Qian, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang
2016· article· en· International Joint Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
79
citations

How this was built: Screen · Findings · About