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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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Healthcare Operations and Scheduling Optimization
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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
fundfunder
venuejournal
aboutaboutness

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

988 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
988 works in the cohort · of 4,299,418page 4 of 20

Labels cover 1 of 988 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 988 of 988 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.

fundno affunlabeled
ORchestra: an online reference database of OR/MS literature in health care
Peter J. H. Hulshof, Richard J. Boucherie, J. Theresia van Essen, Erwin W. Hans, Johann L. Hurink, Nikky Kortbeek +6 more
2011· article· en· Health Care Management Science· Health Professions
machine prediction:candidate · scholarly_communicationconsensus · none
34
citations
affvenueaboutunlabeled
PERFORMANCE ANALYSIS OF THE OPERATING ROOM USING SIMULATION
Qing Niu, Qingjin Peng, Tarek El Mekkawy, Yin Yin Tan, Helga Bruant, Leanne Bernaerdt
2011· article· en· Proceedings of the Canadian Engineering Education Association (CEEA)· Health Professions
machine prediction:candidate · noneconsensus · none
32
citations
affvenueaboutunlabeled
Development of pediatric wait time access targets
James G. Wright, Kayi Li, Cathy Seguin, Marilyn Booth, Peter L. Fitzgerald, Sarah Jones +2 more
2011· article· en· Canadian Journal of Surgery· Health Professions
machine prediction:candidate · noneconsensus · none
31
citations
affno abstractunlabeled
Inventory management of reusable surgical supplies
Adam Diamant, Joseph Milner, Fayez A. Quereshy, Bo Xu
2017· article· en· Health Care Management Science· Health Professions
machine prediction:candidate · noneconsensus · none
30
citations
affaboutunlabeled
Why do surgeons schedule their own surgeries?
David Johnston, Adam Diamant, Fayez A. Quereshy
2019· article· en· Journal of Operations Management· Health Professions
machine prediction:candidate · noneconsensus · none
30
citations
venueno affno abstractunlabeled
Development and assessment of a priority score for cataract surgery
Maria Pia Fantini, Antonella Negro, Stefano Accorsi, Luca Cisbani, Francesco Taroni, Roberto Grilli
2004· article· en· Canadian Journal of Ophthalmology· Health Professions
machine prediction:candidate · noneconsensus · none
29
citations
affunlabeled
Health care operations management
Michael Carter, Erwin W. Hans, Rainer Kolisch
2012· article· en· OR Spectrum· Health Professions
machine prediction:candidate · noneconsensus · none
27
citations

How this was built: Screen · Findings · About