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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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COVID-19 epidemiological studies
Retraction
Abstract
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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
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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 36 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.

affno abstractunlabeled
Knowledge, germs, and output
Shouyong Shi
2022· article· en· Review of Economic Dynamics· Mathematics
machine prediction:candidate · noneconsensus · none
4
citations
afffundno abstractunlabeled
Longitudinal modeling of infectious disease
Alwell J. Oyet, Brajendra C. Sutradhar
2013· article· en· Sankhya B· Mathematics
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
The Economics of Infectious Diseases
M. Christopher Auld, Eli P. Fenichel, Flavio Toxvaerd
2025· article· en· Journal of Economic Literature· Mathematics
machine prediction:candidate · noneconsensus · none
4
citations
venueno affunlabeled
Large-Scale Bibliometric Analysis of Coronavirus
Ali Mustafa Qamar, Rehan Ullah Khan, Suliman A. Alsuhibany
2021· article· en· International Journal of Design & Nature and Ecodynamics· Mathematics
machine prediction:candidate · bibliometricsconsensus · none
4
citations
venueno affunlabeled
MERS differs from SARS, say experts
Carolyn Brown
2014· article· en· Canadian Medical Association Journal· Mathematics
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Engaging private pharmacies to help end TB in India
Ravdeep Gandhi, Khumanthem Deepak, Gajendra Kumar Verma, S. Chaubey, LIKITH KUMAR NUCHINA KUMAR, Joel Shyam Klinton +3 more
2022· article· en· The International Journal of Tuberculosis and Lung Disease· Mathematics
machine prediction:candidate · noneconsensus · none
3
citations
venueno affunlabeled
On COVID-19 Vaccination in Nigeria: An Empirical Study
Olumide S. Adesina, Adedayo F. Adedotun, Nureni Olawale Adeboye, Tolulope F. Adesina, Hilary I. Okagbue, Ayobami F. Gboyega
2023· article· en· International Journal of Design & Nature and Ecodynamics· Mathematics
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Estimation of COVID-19 burden in Egypt – Authors' reply
Ashleigh R. Tuite, Victoria Ng, Erin E. Rees, David N. Fisman, Annelies Wilder‐Smith, Kamran Khan +1 more
2020· letter· en· The Lancet Infectious Diseases· Mathematics
machine prediction:candidate · noneconsensus · none
3
citations
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How this was built: Screen · Findings · About