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
Bayesian Modeling and Causal 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.

961 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.
961 works in the cohort · of 4,299,418page 12 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.

affno abstractunlabeled
Bayesian Network Inference Using Marginal Trees
Cory J. Butz, Jhonatan de S. Oliveira, Anders L. Madsen
2014· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affvenueunlabeled
Correlation in a Bayesian framework
Anirban Dasgupta, George Casella, Mohan Delampady, Christian Genest, William E. Strawderman, Herman Rubin
2000· article· en· Canadian Journal of Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Expert-Aided Causal Discovery of Ancestral Graphs
Tiago da Silva, António Góis, Dominik Heider, Samuel Kaski, Diego Mesquita, Adèle Ribeiro
2023· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Surprise-Based Qualitative Probabilistic Networks
Zina Ibrahim, Ahmed Y. Tawfik, Alioune Ngom
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Probabilistic and Bayesian Networks
Ke-Lin Du, M. N. S. Swamy
2019· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Artificial K-lines
Anestis A. Toptsis, Alexander Dubitski
2009· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Decision making in large-scale domains: a case study
Mikhail Soutchanski, Huy Pham, John Mylopoulos
2006· article· en· European Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
On Darwinian Networks
Cory J. Butz, Jhonatan S. Oliveira, André E. dos Santos
2017· article· en· Computational Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
On Fast arc-reversal
Cory J. Butz, Anders L. Madsen, Jhonatan S. Oliveira
2025· article· en· International Journal of Approximate Reasoning· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Causal diagrams for disease latency bias
Mahyar Etminan, Ramin Rezaeianzadeh, Mohammad Alì Mansournia
2024· article· en· International Journal of Epidemiology· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Model Counting in the Wild
Arijit Shaw, Kuldeep S. Meel
2024· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Dependency Structure Discovery from Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Bernhard Schölkopf, Michael C. Mozer +3 more
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Simple Propagation with Arc-Reversal in Bayesian Networks
Anders L. Madsen, Cory J. Butz, Jhonatan de S. Oliveira, André E. dos Santos
2018· article· en· VBN Forskningsportal (Aalborg Universitet)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Decision Sum-Product-Max Networks
Mazen Melibari, Pascal Poupart, Prashant Doshi
2016· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Causal Discovery
Hong Yao, Cory J. Butz, Howard J. Hamilton
2006· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Darwinian Networks
Cory J. Butz, Jhonatan S. Oliveira, André E. dos Santos
2015· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About