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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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Global Maternal and Child Health
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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
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

4,411 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.
4,411 works in the cohort · of 4,299,418page 73 of 89

Labels cover 31 of 4,411 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 4,411 of 4,411 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.

venueaboutno affunlabeled
From the Editor-in-Chief
John R. Paul
2007· article· en· World health & population· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0029-7437(05)70296-2
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
fundno affunlabeled
Nurse Practitioners Driving Virtual Postpartum Care
Leah Spiro, Galen Cook‐Wiens, Wei-Ti Chen, Nancy Jo Bush, Margo Minissian
2025· article· en· The Journal for Nurse Practitioners· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
10.3917/reco.pr2.0139
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
10.1016/s1155-1976(96)07874-6
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
Occurrence Download
2023· dataset· en· Global Biodiversity Information Facility· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
venueno affunlabeled
Problems And Complexities Of Caesarean Section And The Effective Role Of Nursing
Fatemeh Draia Alrasheedi, Mashael Musharrid Okash Alanazi, Salma Mohammed Hadi Al Harbi, Noha Mohammed Kalf Alenzi, Majed Noor Faleh Al Asaadi, Khalid Abdullah Mohammed Bani Humayyim +1 more
2023· article· en· Journal of Survey in Fisheries Sciences· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Maternal Mortality
Shi Wu Wen, Ruili Xie
2014· book-chapter· en· Elsevier eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affgemma · no categorygpt · no categorymodels agree
For richer, for poorer
Ronald Labonté, Ted Schrecker, Amit Gupta
2005· article· en· BMJ· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About