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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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Viral Infections and Outbreaks Research
Retraction
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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.

1,563 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,563 works in the cohort · of 4,299,418page 24 of 32

Labels cover 3 of 1,563 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 1,563 of 1,563 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.

afffundunlabeled
Rodent adapted marburg viruses are lethal in ferrets
Zachary Schiffman, Lauren Garnett, Kaylie N. Tran, Jonathan Audet, Kevin Tierney, Kim Azaransky +3 more
2025· article· en· npj Viruses· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
The predicament of patients with suspected Ebola
Felicity Fitzgerald, David Baion, Kevin Wing, Shunmay Yeung, Foday Sahr
2017· letter· en· The Lancet Global Health· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Review of candidate vaccines for the prevention of Lassa fever
Olga Popova, Olga V. Zubkova, Tatiana A. Ozharovskaia, Denis I. Zrelkin, Daria V. Voronina, Inna V. Dolzhikova +4 more
2021· review· en· Problems of Virology· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
venueno affunlabeled
Un cahier d’histoire d’Ebola
Michèle Cros, Benjamin Frerot
2023· article· fr· Anthropologie et Sociétés· Medicine
machine prediction:candidate · noneconsensus · none
1
citations
affvenueno abstractunlabeled
Introduction: Global Vaccine Logics
Janice Graham, Oumy Thiongane
2024· article· en· Anthropologica· Medicine
machine prediction:candidate · stsconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s0000-0000(04)47693-1
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affunlabeled
Erreur de traduction
Yv Bonnier Viger
2002· article· fr· Canadian Medical Association Journal· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/j.ypdi.2015.02.012
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
ID: 68
Eleanor N. Fish, Stephen D.S. McCarthy, Thomas Hoenen, Donald R. Branch
2015· article· en· Cytokine· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Preparing for Pandemics
David Longworth, Frank Milne
2022· book· en· WORLD SCIENTIFIC eBooks· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About