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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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Bayesian Modeling and Causal Inference
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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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aboutaboutness

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

961 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.
961 works in the cohort · of 4,299,418page 8 of 20

Labels cover 2 of 961 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 961 of 961 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.

afffundno abstractunlabeled
Bayesian network inference using marginal trees
Cory J. Butz, Jhonatan S. Oliveira, Anders L. Madsen
2015· article· en· International Journal of Approximate Reasoning· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Classification Methods
Aijun An
2009· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
PIS: a probabilistic inference system
Keith C. C. Chan, Andrew K. C. Wong
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Does Invariant Risk Minimization Capture Invariance
Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
2021· article· en· International Conference on Artificial Intelligence and Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Balancing and Elimination of Nuisance Variables
Siamak Noorbaloochi, David Nelson, Masoud Asgharian
2010· article· en· The International Journal of Biostatistics· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affaboutunlabeled
Assessment of the Tadmus DSS with Work Domain Analysis
Catherine M. Burns, David Bryant, Bruce A. Chalmers
2002· article· en· Proceedings of the Human Factors and Ergonomics Society Annual Meeting· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
fundno affno abstractunlabeled
Does non-correlation imply non-causation?
Eric Neufeld, Sonje Kristtorn
2006· article· en· International Journal of Approximate Reasoning· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundRetractionunlabeled
Investigative advising: a job for Bayes
Jared C. Allen
2014· article· en· Crime Science· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
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