MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
Gaussian Processes and Bayesian Inference
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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
venuejournal
aboutaboutness

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 ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
309 works in the cohort · of 4,299,418page 1 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.

afffundunlabeled
Gaussian Process Dynamical Models for Human Motion
Jonathan M. Wang, David J. Fleet, Aaron Hertzmann
2007· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1,038
citations
affunlabeled
Gaussian Process Dynamical Models
Jianguo Wang, Aaron Hertzmann, David M. Blei
2005· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
432
citations
affunlabeled
Deep Neural Networks as Gaussian Processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl‐Dickstein
2018· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
318
citations
affunlabeled
Robust online Hamiltonian learning
Christopher Granade, Christopher Ferrie, Nathan Wiebe, David G. Cory
2012· article· en· New Journal of Physics· Computer Science
machine prediction:candidate · noneconsensus · none
207
citations
afffundunlabeled
Global Coordination of Local Linear Models
Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton
2001· article· en· ScholarlyCommons (University of Pennsylvania)· Computer Science
machine prediction:candidate · noneconsensus · none
177
citations
affno abstractunlabeled
Nested sampling for physical scientists
G. Ashton, Noam Bernstein, Johannes Büchner, Xi Chen, Gábor Cśanyi, Andrew Fowlie +17 more
2022· article· en· Nature Reviews Methods Primers· Computer Science
machine prediction:candidate · noneconsensus · none
151
citations
affunlabeled
Practical Bayesian tomography
Christopher Granade, Joshua Combes, David G. Cory
2016· article· en· New Journal of Physics· Computer Science
machine prediction:candidate · noneconsensus · none
106
citations
affunlabeled
Gated Softmax Classification
Roland Memisevic, Christopher Zach, Marc Pollefeys, Geoffrey E. Hinton
2010· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
79
citations
affunlabeled
Parallel Bayesian Additive Regression Trees
Matthew T. Pratola, Hugh Chipman, James Gattiker, David Higdon, Robert McCulloch, William N. Rust
2014· article· en· Journal of Computational and Graphical Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
64
citations
affunlabeled
Bayesian actor-critic algorithms
Mohammad Ghavamzadeh, Yaakov Engel
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affunlabeled
The Gaussian Process Density Sampler
Iain Murray, David Mackay, Ryan P. Adams
2008· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
48
citations
affunlabeled
Fast Krylov Methods for N-Body Learning
Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang
2005· article· en· Oxford University Research Archive (ORA) (University of Oxford)· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
Self Supervised Boosting
Max Welling, Richard S. Zemel, Geoffrey E. Hinton
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations

How this was built: Screen · Findings · About