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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.

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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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Meta-analysis and systematic reviews
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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.

4,076 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.
4,076 works in the cohort · of 4,299,418page 58 of 82

Labels cover 359 of 4,076 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 4,076 of 4,076 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 abstractgemma · no categorygpt · no categorymodels agree
Research Monitor
2014· article· en· Reference Reviews· Decision Sciences
machine prediction:candidate · metaresearch+insufficient_payloadconsensus · none
1
citations
venueno affunlabeled
The best type of trial
Judy Wright
2004· article· en· Canadian Medical Association Journal· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
1
citations
affno abstractunlabeled
How Much Research is Enough?
Susanna Galbraith
2017· article· en· Weave Journal of Library User Experience· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
1
citations
affunlabeled
Psychometrics
Vivek Panchapakesan, Toni Zhong, Christopher R. Forrest, Andrea L. Pusic
2014· article· en· Plastic & Reconstructive Surgery Global Open· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
Building Credibility with Comprehensive Citation Practices
Pavithra Subramanian, Raghuraman Soundararajan, Jyotsna Makol
2025· article· en· Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
1
citations
affno abstractunlabeled
Conduct and reporting of multivariate network meta-analyses: a scoping review
Jessica J Bartoszko, Mayra Gutiérrez García, Juan Pablo Díaz Martinez, Sergey Yegorov, Romina Brignardello‐Petersen, Dominik Mertz +2 more
2023· review· en· Journal of Clinical Epidemiology· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
1
citations
affunlabeled
Clinical Trials in Orthopedic Surgery
Alexandra Kirkley, Robert G. Marx, S. Griffin, Warren R. Dunn
2005· article· en· Sports Medicine and Arthroscopy Review· Decision Sciences
machine prediction:candidate · metaresearchconsensus · none
1
citations
affaboutunlabeled
Reply to Letter
Jeffrey Barkun
2014· letter· en· Annals of Surgery· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Updates to the standardized reporting guidelines endorsed by the Journal
Donald R. Miller, François Donati, Penelope M. A. Brasher, Scott Beattie, C. David Mazer
2009· article· en· Canadian Journal of Anesthesia/Journal canadien d anesthésie· Decision Sciences
machine prediction:candidate · metaresearchconsensus · metaresearch
1
citations
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