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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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COVID-19 epidemiological studies
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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.

3,341 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.
3,341 works in the cohort · of 4,299,418page 2 of 67

Labels cover 12 of 3,341 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 3,341 of 3,341 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.

afffundno abstractunlabeled
Predicting epidemics on directed contact networks
Lauren Ancel Meyers, M. E. J. Newman, Babak Pourbohloul
2005· article· en· Journal of Theoretical Biology· Mathematics
machine prediction:candidate · noneconsensus · none
287
citations
afffundunlabeled
Social Factors in Epidemiology
Chris T. Bauch, Alison P. Galvani
2013· article· en· Science· Mathematics
machine prediction:candidate · noneconsensus · none
257
citations
affno abstractunlabeled
The SEIRS model for infectious disease dynamics
Ottar N. Bjørnstad, Katriona Shea, Martin Krzywinski, Naomi Altman
2020· article· en· Nature Methods· Mathematics
machine prediction:candidate · noneconsensus · none
237
citations
afffundno abstractunlabeled
On the evolutionary epidemiology of SARS-CoV-2
Troy Day, Sylvain Gandon, Sébastien Lion, Sarah P. Otto
2020· article· en· Current Biology· Mathematics
machine prediction:candidate · noneconsensus · none
226
citations
afffundunlabeled
The Failure of <i>R</i><sub>0</sub>
Jing Li, Daniel Blakeley, Robert J. Smith
2011· review· en· Computational and Mathematical Methods in Medicine· Mathematics
machine prediction:candidate · noneconsensus · none
226
citations
afffundunlabeled
Optimal COVID-19 quarantine and testing strategies
Chad R. Wells, Jeffrey P. Townsend, Abhishek Pandey, Seyed M. Moghadas, Gary R. Krieger, Burton H. Singer +3 more
2021· article· en· Nature Communications· Mathematics
machine prediction:candidate · noneconsensus · none
226
citations
affaboutunlabeled
Modeling and Dynamics of Infectious Diseases
Zhien Ma, Yicang Zhou, Jianhong Wu
2009· book· en· Series in contemporary applied mathematics· Mathematics
machine prediction:candidate · noneconsensus · none
218
citations
affunlabeled
Geographies of the COVID-19 pandemic
Reuben Rose‐Redwood, Rob Kitchin, Elia Apostolopoulou, Lauren Rickards, Tyler Blackman, Jeremy W. Crampton +2 more
2020· article· en· Dialogues in Human Geography· Mathematics
machine prediction:candidate · noneconsensus · none
197
citations

How this was built: Screen · Findings · About