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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.

37 results · 1 filter active ·
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20162025
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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.
37 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 37 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 37 of 37 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.

affunlabeled
Report on the Marine Imaging Workshop 2017
Timm Schoening, Jennifer M. Durden, Inken Preuss, Alexandra Branzan Albu, Autun Purser, Bart De Smet +12 more
2017· article· en· Research Ideas and Outcomes· Environmental Science
machine prediction:candidate · noneconsensus · none
18
citations
fundno affunlabeled
Data Management Plan: HarassMap
Reem Wael
2017· article· en· Research Ideas and Outcomes· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
7
citations
affunlabeled
Training and hackathon on building biodiversity knowledge graphs
Joel L. Sachs, Roderic Page, Steve Baskauf, Jocelyn Pender, Beatriz E. Lujan Toro, James Macklin +1 more
2019· article· en· Research Ideas and Outcomes· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · noneconsensus · none
6
citations
aboutno affunlabeled
Data Management Plan: Brazil's Virtual Herbarium
Dora Ann Lange Canhos
2017· article· en· Research Ideas and Outcomes· Biochemistry, Genetics and Molecular Biology
machine prediction:candidate · open_scienceconsensus · none
5
citations
fundno affunlabeled
Case Study: HarassMap
Cameron Neylon
2017· article· en· Research Ideas and Outcomes· Social Sciences
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About