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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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Machine Learning in Healthcare
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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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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,008 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.
1,008 works in the cohort · of 4,299,418page 12 of 21

Labels cover 4 of 1,008 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 1,008 of 1,008 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
A synthetic dataset of liver disorder patients
Giovanna Nicora, Tommaso Mario Buonocore, Enea Parimbelli
2023· article· en· Data in Brief· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
LLM Dataset Inference: Did you train on my dataset?
Pratyush Maini, Hengrui Jia, Nicolas Papernot, Adam Dziedzic
2024· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrow+research_integrity+insufficient_payloadconsensus · none
2
citations
affunlabeled
Expert-augmented machine learning
Efstathios D. Gennatas, Jerome H. Friedman, Lyle Ungar, Romain Pirracchio, Eric Eaton, Lara Reichman +1 more
2020· preprint· en· Proceedings of the National Academy of Sciences· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Intelligent Decision Support in Medicine: back to Bayes?
Bill Winogron, Bowen Hui, Catherine Pyper, Colin Jones, Craig Boutilier, Gitte Lindgaard +5 more
2020· article· en· TUGraz OPEN Library (Graz University of Technology)· Computer Science
distilled prediction:candidate · open_science+insufficient_payloadconsensus · none
2
citations
affno abstractunlabeled
AI listens for health conditions
Neil Savage
2025· article· en· Nature· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
afffundno abstractunlabeled
RIMD: A novel method for clinical prediction
Saroj Basnet, Sirvan Parasteh, Alireza Manashty, Brandon Sasyniuk
2023· article· en· Artificial Intelligence in Medicine· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affunlabeled
HippUnfold HCP-YA Training Data
Jordan DeKraker, Ali R. Khan
2022· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+open_science+insufficient_payloadconsensus · open_science+insufficient_payload
2
citations

How this was built: Screen · Findings · About