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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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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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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 1 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
The Optimality of Naive Bayes.
Harry Zhang
2004· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1,402
citations
affunlabeled
Toward Causal Representation Learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal +1 more
2021· article· en· Proceedings of the IEEE· Computer Science
machine prediction:candidate · noneconsensus · none
1,024
citations
affno abstractunlabeled
A new distance between two bodies of evidence
Anne-Laure Jousselme, Dominic Grenier, Éloi Bossé
2001· article· en· Information Fusion· Computer Science
machine prediction:candidate · noneconsensus · none
913
citations
affunlabeled
Context-Specific Independence in Bayesian Networks
Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller
2013· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
557
citations
affunlabeled
SPUDD: Stochastic Planning using Decision Diagrams
Jesse Hoey, Robert St‐Aubin, Alan J. Hu, Craig Boutilier
2013· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
394
citations
venueno affunlabeled
The Logic of Deep Disagreements
Robert J. Fogelin
2005· article· en· Informal Logic· Computer Science
machine prediction:candidate · noneconsensus · none
191
citations
affunlabeled
Checking for prior-data conflict
Michael Evans, Hadas Moshonov
2006· article· en· Bayesian Analysis· Computer Science
machine prediction:candidate · metaresearchconsensus · none
185
citations
affno abstractunlabeled
The Cross-Entropy Method for Optimization
Zdravko I. Botev, Dirk P. Kroese, Reuven Y. Rubinstein, Pierre L’Ecuyer
2013· book-chapter· en· Handbook of statistics· Computer Science
machine prediction:candidate · noneconsensus · none
176
citations
affno abstractunlabeled
Naive Bayesian Classifiers for Ranking
Harry Zhang, Su Jiang
2004· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
119
citations
affunlabeled
Context-Specific Independence in Bayesian Networks
Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller
2013· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
99
citations
affunlabeled
Hidden naive Bayes
Harry Zhang, Liangxiao Jiang, Su Jiang
2005· article· en· National Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
98
citations
affunlabeled
Causal Discovery with Reinforcement Learning
Shengyu Zhu, Ignavier Ng, Zhitang Chen
2019· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
89
citations
afffundunlabeled
Default Priors for Bayesian and Frequentist Inference
D. A. S. Fraser, Nancy Reid, Elisabetta Marras, Grace Y. Yi
2010· article· en· Journal of the Royal Statistical Society Series B (Statistical Methodology)· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations

How this was built: Screen · Findings · About