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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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Uncertainty in Artificial Intelligence
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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.

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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.

35 results · 1 filter active ·
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20042021
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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.
35 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 35 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 35 of 35 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
From fields to trees
Firas Hamze, Nando de Freitas
2004· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
45
citations
affunlabeled
Low Frequency Adversarial Perturbation
Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
2018· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Off-policy TD(λ) with a true online equivalence
Hado van Hasselt, A. Rupam Mahmood, Richard S. Sutton
2014· article· en· Uncertainty in Artificial Intelligence· Engineering
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Triply Stochastic Gradients on Multiple Kernel Learning.
Xiang Li, Bin Gu, Shuang Ao, Huaimin Wang, Charles X. Ling
2017· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Amortized Bayesian Optimization over Discrete Spaces
Kevin Swersky, Yulia Rubanova, David Dohan, Kevin J. Murphy
2020· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
State sequence analysis in hidden Markov models
Yuri Grinberg, Theodore J. Perkins
2015· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Probability Distillation: A Caveat and Alternatives.
Chin-Wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville
2019· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Semi-supervised Sequential Generative Models
Michael N. Teng, Tuan Anh Lê, Adam Ścibior, Frank Wood
2020· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Identifying Regions of Trusted Predictions
Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner
2021· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

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