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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 diagnosis using AI
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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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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.

1,016 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.
1,016 works in the cohort · of 4,299,418page 18 of 21

Labels cover 5 of 1,016 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 1,016 of 1,016 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
SARS CoV 2 Evolution Manuscript Processed Files and Code
Julian Willett, Annie Gravel, Isabelle Dubuc, Leslie Gudimard, Paul R. Fortin, Inés Colmegna +6 more
2024· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
rc59 fillable pdf
2024· other· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
aboutno affunlabeled
How To Deal With Temptations
2010· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/s1169-7768(95)32281-3
2000· article· en· Time to knit· Medicine
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Dataset for Leeuwis et al. (2020)
Robine H. J. Leeuwis, Fábio S. Zanuzzo, Ellen de Fátima C. Peroni, A. Kurt Gamperl
2020· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Multilayer electrophysiological source imaging
Ariosky Areces-González, Deirel Paz-Linares, Claude Lepage, Lindsay B. Lewis, P.-J. Toussaint, Alan C. Evans +2 more
2023· article· en· International Journal of Psychophysiology· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
pha4ge/SARS-CoV-2-Contextual-Data-Specification: Release V 2.0.1
Inês Mendes, griffie, Finlay Maguire, Bede Constantinides, Duncan MacCannell, Nabil-Fareed Alikhan
2021· article· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
What A Young Wife Ought To Know (Part 2)
2019· other· en· Bulletin of Miscellaneous Information (Royal Gardens Kew)· Medicine
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Symptom Based Models of COVID-19 Infection Using AI
Songqiao Liu, Yuan Hong Sun, Alex Waese-Perlman, Nathan Yee Lee, Haibo Zhang, Kang Lee
2022· book-chapter· en· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About