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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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Gaussian Processes and Bayesian 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.

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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.

309 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.
309 works in the cohort · of 4,299,418page 3 of 7

Labels cover 0 of 309 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 309 of 309 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
Wormholes Improve Contrastive Divergence
Max Welling, Andriy Mnih, Geoffrey E. Hinton
2003· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
13
citations
affunlabeled
Harmonic Exponential Families on Manifolds
Taco Cohen, Max Welling
2015· article· en· UvA-DARE (University of Amsterdam)· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
fundno affunlabeled
Gaze Following as Goal Inference: A Bayesian Model
Abram L. Friesen, Rajesh P. N. Rao
2011· article· en· eScholarship (California Digital Library)· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Action selection for hammer shots in curling
Zaheen Farraz Ahmad, Robert C. Holte, Michael Bowling
2016· article· en· International Joint Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel P. Bruinsma, Eric Perim, William Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner
2020· article· en· Aaltodoc (Aalto University)· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
A Sober Look at Spectral Learning
Han Zhao, Pascal Poupart
2014· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Kernel-Based Copula Processes
Sebastian Jaimungal, Eddie K. H. Ng
2009· article· en· SSRN Electronic Journal· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Data Fusion Using Weighted Likelihood
Pengfei Guo, Xiaogang Wang, Yuehua Wu
2012· article· en· European Journal of Pure and Applied Mathematics· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Emerging Directions in Bayesian Computation
Steven L. Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson
2024· article· en· Statistical Science· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
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