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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
fundfunder
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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.

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 9 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.

affunlabeled
Causal learning without DAGs
David Duvenaud, Daniel Eaton, Mark Schmidt
2008· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
An Algebraic Framework for Bayes Nets of Time Series
Peter E. Caines, Henry P. Wynn
2007· book-chapter· en· Lecture notes in control and information sciences· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Reference classes and relational learning
Michael Chiang, David Poole
2011· article· en· International Journal of Approximate Reasoning· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Latent Complementarity in Bundles Models
R. G. D. Allen, John Rehbeck
2018· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
A Quadratic Kalman Filter
Alain Monfort, Jean‐Paul Renne, Guillaume Roussellet
2013· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
LEARNING DECISION TREES WITH LOG CONDITIONAL LIKELIHOOD
Liang Han, Yuhong Yan, Harry Zhang
2010· article· en· International Journal of Pattern Recognition and Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
Aristotle: stratified causal discovery for omics data
Mehrdad Mansouri, Sahand Khakabimamaghani, Leonid Chindelevitch, Martin Ester
2022· article· en· BMC Bioinformatics· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
Higher Order Bayesian Networks, Exactly
Claudia Faggian, Daniele Pautasso, Gabriele Vanoni
2024· article· en· Proceedings of the ACM on Programming Languages· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Probabilistic Relational Models
Professor Reda Alhajj, Professor Jon Rokne
2014· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Learning Branching Heuristics for Propositional Model Counting
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger Grosse, Sanjit A. Seshia +1 more
2021· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Optimal set recommendations based on regret
Paolo Viappiani, Craig Boutilier
2009· article· en· Web Personalization and Recommender Systems· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Conditional Beliefs and Higher-Order Preferences
Byung Soo Lee
2013· article· en· Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
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