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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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Data-Driven Disease Surveillance
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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.

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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.

1,315 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.
1,315 works in the cohort · of 4,299,418page 2 of 27

Labels cover 11 of 1,315 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 1,315 of 1,315 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
Digital public health surveillance: a systematic scoping review
Zahra Shakeri Hossein Abad, Adrienne Kline, Madeena Sultana, Mohammad Noaeen, Elvira Nurmambetova, Filipe R. Lucini +2 more
2021· article· en· npj Digital Medicine· Medicine
machine prediction:candidate · noneconsensus · none
96
citations
affno abstractunlabeled
The need for GIScience in mapping COVID-19
Leah Rosenkrantz, Nadine Schuurman, Nathaniel Bell, Ofer Amram
2020· article· en· Health & Place· Medicine
machine prediction:candidate · noneconsensus · none
94
citations
affno abstractunlabeled
An overview of Internet biosurveillance
David Hartley, Noele P. Nelson, Ray R. Arthur, Philippe Barboza, Nigel Collier, N. F. Lightfoot +10 more
2013· review· en· Clinical Microbiology and Infection· Medicine
machine prediction:candidate · noneconsensus · none
75
citations
affunlabeled
An open repository of real-time COVID-19 indicators
Alex Reinhart, Logan Brooks, Maria Jahja, Aaron Rumack, Jingjing Tang, Sumit Agrawal +61 more
2021· article· en· Proceedings of the National Academy of Sciences· Medicine
machine prediction:candidate · open_scienceconsensus · none
72
citations
fundno affunlabeled
Google Trends can improve surveillance of Type 2 diabetes
Nataliya Tkachenko, Sarunkorn Chotvijit, Neha Gupta, Emma Bradley, Charlotte Gilks, Weisi Guo +5 more
2017· article· en· Scientific Reports· Medicine
machine prediction:candidate · noneconsensus · none
69
citations
aboutno affunlabeled
Disease Surveillance on Complex Social Networks
José Luís Herrera, Ravi Srinivasan, John S. Brownstein, Alison P. Galvani, Lauren Ancel Meyers
2016· article· en· PLoS Computational Biology· Medicine
machine prediction:candidate · noneconsensus · none
66
citations
affunlabeled
The landscape of international event-based biosurveillance
David Hartley, Noele P. Nelson, Ronald A. Walters, Ray Arthur, Roman Yangarber, Larry Madoff +6 more
2010· article· en· Emerging Health Threats Journal· Medicine
machine prediction:candidate · noneconsensus · none
57
citations

How this was built: Screen · Findings · About