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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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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
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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.

1,563 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.
1,563 works in the cohort · of 4,299,418page 11 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.

fundno affunlabeled
Evaluating the frequency of asymptomatic Ebola virus infection
Placide Mbala, Marc Baguelin, Ipos Ngay, Alicia Roselló, Prime Mulembakani, Nikolaos Demiris +2 more
2017· article· en· Philosophical Transactions of the Royal Society B Biological Sciences· Medicine
machine prediction:candidate · noneconsensus · none
24
citations
affaboutno abstractunlabeled
Stopping epidemics when and where they occur
Els Torreele, Michel D. Kazatchkine, Joanne Liu, Mark Dybul, Mauricio Cárdenas, Sudhvir Singh +5 more
2023· article· en· The Lancet· Medicine
machine prediction:candidate · noneconsensus · none
24
citations
affvenueno abstractunlabeled
SARS: A Local Public Health Perspective
Sheela V. Basrur, Barbara Yaffe, Bonnie Henry
2004· article· en· Canadian Journal of Public Health· Medicine
machine prediction:candidate · noneconsensus · none
24
citations
fundno affunlabeled
Seroprevalence of Wenzhou virus in China
Li Guo, Shasha Liu, Jingdong Song, Lianlian Han, Hu Zhang, Chao Wu +3 more
2020· article· en· Biosafety and Health· Medicine
machine prediction:candidate · noneconsensus · none
22
citations
afffundno abstractunlabeled
Clinical Evaluation of Ebola Virus Disease Therapeutics
Guodong Liu, Gary Wong, Shuo Su, Yuhai Bi, Frank Plummer, George F. Gao +2 more
2017· review· en· Trends in Molecular Medicine· Medicine
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Stochasticity and the limits to confidence when estimating <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si0040.gif" overflow="scroll"><mml:msub subscriptshift="65%"><mml:mrow><mml:mi mathvariant="script">R</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> of Ebola and other emerging infectious diseases
Bradford P. Taylor, Jonathan Dushoff, Joshua S. Weitz
2016· article· lv· Journal of Theoretical Biology· Medicine
machine prediction:candidate · noneconsensus · none
21
citations
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How this was built: Screen · Findings · About