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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.

7,507 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.
7,507 works in the cohort · of 4,299,418page 1 of 151

Labels cover 35 of 7,507 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 7,507 of 7,507 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.

affaboutunlabeled
Toronto emotional speech set (TESS)
M. Kathleen Pichora‐Fuller, Kate Dupuis
2020· dataset· en· Borealis· Psychology
machine prediction:candidate · noneconsensus · none
209
citations
affunlabeled
2011 - 2021 OA APCs
2021· dataset· en· Borealis
machine prediction:candidate · metaresearch+bibliometricsconsensus · none
6
citations
affunlabeled
COVID-19 Twitter Dataset
2020· dataset· en· Borealis
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
B-TIM snow for JRA55
2024· dataset· en· Borealis· Environmental Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Special Feature: Can habitat selection predict abundance?
Mark S. Boyce, Chris J. Johnson, Evelyn H. Merrill, Scott E. Nielsen, Erling J. Solberg, Bram Van Moorter
2015· dataset· en· Borealis· Environmental Science
machine prediction:candidate · noneconsensus · none
2
citations
affaboutunlabeled
Global Salt Experiment
2021· dataset· en· Borealis
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
SEEDNet Library
2024· dataset· en· Borealis
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
OCTID citation
2018· dataset· en· Borealis
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
WNED datasets and results
Zhaochen Guo, Denilson Barbosa
2017· dataset· en· Borealis· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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