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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.

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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 16 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.

aboutno affunlabeled
Impact of Using ChatGPT on Students
Robee Roshna Zia
2025· book-chapter· en· Advances in computational intelligence and robotics book series· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Artificial intelligence and its contribution to overcome COVID-19
Arun Chockalingam, Vibha Tyagi, Rahul G. Krishnan, ShehrozS Khan, Sarath Chandar, MirzaFaisal Beg +8 more
2021· article· en· International Journal of Noncommunicable Diseases· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affaboutunlabeled
CanESM5 data for CCCma COVID-19 climate scenarios
John C. Fyfe, Viatcheslav Kharin, Neil C. Swart, Gregory M. Flato, Michael Sigmond, Nathan P. Gillett
2020· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Corona Discharge Ionization
Kermit K. Murray, Robert K. Boyd, Marcos N. Eberlin, G. John Langley, Liang Li, Yasuhide Naito
2016· dataset· en· IUPAC Standards Online· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
aboutno affunlabeled
REVIEW ARTICLE: EPIDEMIOLOGY OF COVID’19
Idahor Courage, Okuma Oghenevwede, Anugom Gene-Genald, Willie Gabriel, Akwazie Chukwunonso, Boluwatife Oyetayo
2020· article· en· American Journal of Health Medicine and Nursing Practice· Medicine
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII (brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography)
Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, María A. Zuluaga, Shuai Zhou +2 more
2020· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Medicine
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About