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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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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 6 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
Wiley Series in Probability and Statistics
A. K. Md. Ehsanes Saleh
2005· other· en· Wiley series in probability and statistics· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Learning to Discover Sparse Graphical Models
Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew B. Blaschko
2016· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Hindsight Optimization for Hybrid State and Action MDPs
Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern
2017· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Explaining Naive Bayes Classifications
Russell Greiner, Brett Poulin, Paul Lu, Zhonghua Lu, Cam Macdonell, David S. Wishart +2 more
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affno abstractunlabeled
Dynamic multiagent probabilistic inference
Xiangdong An, Yang Xiang, Nick Cercone
2007· article· en· International Journal of Approximate Reasoning· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
fundno affunlabeled
[no title]
Sindy Löwe, David Madras, Richard S. Zemel, Max Welling
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
12
citations
affunlabeled
Explaining recommendations generated by MDPs
Omar Zia Khan, Pascal Poupart, James P. Black
2008· article· en· Explanation-aware Computing· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
A NOVEL DISTANCE FUNCTION: FREQUENCY DIFFERENCE METRIC
Liangxiao Jiang, Chaoqun Li, Harry Zhang, Zhihua Cai
2014· article· en· International Journal of Pattern Recognition and Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
First Order Probabilistic Logic
Brigitte Jaumard, Alexandre Fortin, Md. Istiaque Shahriar, Razia Sultana
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Fast d-DNNF compilation with sharpSAT
Christian Muise, Sheila A. McIlraith, J. Christopher Beck, Eric Hsu
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations

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