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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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Artificial Intelligence in Healthcare and Education
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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.

3,498 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.
3,498 works in the cohort · of 4,299,418page 10 of 70

Labels cover 44 of 3,498 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 3,498 of 3,498 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.

affunlabeled
Effect of Chat GPT on the digitized learning process of university students
Alejandro Guadalupe Rincón Castillo, Giovanna Jackeline Serna Silva, Javier Pedro Flores Arocutipa, Haydeé Quispe Berríos, Marco Antonio Marcos Rodríguez, Guillermo Yanowsky Reyes +4 more
2023· article· en· Journal of Namibian Studies History Politics Culture· Medicine
machine prediction:candidate · noneconsensus · none
44
citations
affno abstractunlabeled
Registries: Big data, bigger problems?
Luc Rubinger, Seper Ekhtiari, Aaron Gazendam, Mohit Bhandari
2021· article· en· Injury· Medicine
machine prediction:candidate · metaresearchconsensus · none
44
citations
affno abstractunlabeled
Advances in Artificial Intelligence
Howard J. Hamilton
2000· book· en· Lecture notes in computer science· Medicine
machine prediction:candidate · noneconsensus · none
42
citations
venueno affunlabeled
Machine learning in medicine
Chloe Gui, Victoria Chan
2017· article· en· University of Western Ontario Medical Journal· Medicine
machine prediction:candidate · noneconsensus · none
41
citations
affno abstractunlabeled
Prompt engineering when using generative AI in nursing education
Siobhán O’Connor, Laura‐Maria Peltonen, Maxim Topaz, Lu‐Yen Anny Chen, Martin Michalowski, Charlene Ronquillo +4 more
2023· editorial· en· Nurse Education in Practice· Medicine
machine prediction:candidate · metaresearchconsensus · none
39
citations

How this was built: Screen · Findings · About