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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
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 ·
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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 11 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.

afffundunlabeled
Exploiting Determinism to Scale Relational Inference
Mohamed Hamza Ibrahim, Christopher Pal, Gilles Pesant
2015· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
ApproxML
Sona Hasani, Faezeh Ghaderi, Shohedul Hasan, Saravanan Thirumuruganathan, Abolfazl Asudeh, Nick Koudas +1 more
2019· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
venueno affunlabeled
Réseaux GAI pour la prise de décision
Christophe Gonzales, Patrice Perny, Sergio Queiroz
2007· article· fr· Revue d intelligence artificielle· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Mode Poset Probability Polytopes
Guido Montúfar, Johannes Rauh
2016· preprint· en· Journal of Algebraic Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Properties of Weak Conditional Independence
Cory J. Butz, Manon J. Sanscartier
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Decomposing Gaussians with unknown covariance
Ameer Dharamshi, Anna Neufeld, Linlin Gao, James P. Bien, Daniela Witten
2025· article· en· Biometrika· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Decayed MCMC Filtering
Bhaskara Marthi, Hanna Pasula, Stuart Russell, Yuval Peres
2012· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
Agent Incentives: A Causal Perspective
Tom Everitt, Ryan M. Carey, Eric Langlois, Pedro A. Ortega, Shane Legg
2021· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Many valued probability theory
Charles G. Morgan
2004· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Testing Ising Models
Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
2018· preprint· en· IEEE Transactions on Information Theory· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
What are the points?
Jean-François Delannoy
2001· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Application of Subjective Logic to Health Research Surveys
Robert D. Kent, Jason McCarrell, Gilles Paquette, Bryan St. Amour, Ziad Kobti, Anne Snowdon
2010· book-chapter· en· Smart innovation, systems and technologies· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Compiling with Generating Functions
Jianlin Li, Yizhou Zhang
2025· article· en· Proceedings of the ACM on Programming Languages· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affvenueaboutunlabeled
Hard Decisions about Fundamental Values
Andreas Laupacis
2004· review· en· A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About