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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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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
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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.

113 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
113 works in the cohort · of 4,299,418page 1 of 3

Labels cover 0 of 113 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 113 of 113 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
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan
2009· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
3,486
citations
affunlabeled
A survey on semi-supervised learning
Jesper E. van Engelen, Holger H. Hoos
2019· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
2,562
citations
affno abstractunlabeled
An Introduction to MCMC for Machine Learning
Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan
2003· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
2,418
citations
affno abstractunlabeled
Inference for the Generalization Error
Claude Nadeau
2003· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
959
citations
affno abstractunlabeled
NP-hardness of Euclidean sum-of-squares clustering
Daniel Aloise, Amit Deshpande, Pierre Hansen, Preyas Popat
2009· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
863
citations
affno abstractunlabeled
Kernel Matching Pursuit
Pascal Vincent, Yoshua Bengio
2002· article· en· Machine Learning· Engineering
machine prediction:candidate · noneconsensus · none
322
citations
affno abstractunlabeled
The class imbalance problem in deep learning
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Branco, Bartosz Krawczyk, Nathalie Japkowicz
2022· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
270
citations
fundno affno abstractunlabeled
Adaptive game AI with dynamic scripting
Pieter Spronck, Marc Ponsen, I.G. Sprinkhuizen-Kuyper, Eric Postma
2006· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
260
citations
affunlabeled
Quantum speed-up for unsupervised learning
Esma Aı̈meur, Gilles Brassard, Sébastien Gambs
2012· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
213
citations
afffundno abstractunlabeled
Bayesian Treed Models
Hugh Chipman, Edward I. George, Robert E. McCulloch
2002· article· en· Machine Learning· Mathematics
machine prediction:candidate · noneconsensus · none
184
citations
affunlabeled
ILP turns 20
Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter Flach, Katsumi Inoue +1 more
2011· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
128
citations
affno abstractunlabeled
Model Selection for Small Sample Regression
Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
2002· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
125
citations
affno abstractunlabeled
Temporal-difference search in computer Go
David Silver, Richard S. Sutton, Martin Müller
2012· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
93
citations
affno abstractunlabeled
Beam search algorithms for multilabel learning
Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan
2013· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
90
citations
affno abstractunlabeled
Arbitrage of forecasting experts
Vítor Cerqueira, Luı́s Torgo, Fábio Pinto, Carlos Soares
2018· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affno abstractunlabeled
Model selection in reinforcement learning
Amir‐massoud Farahmand, Csaba Szepesvári
2011· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
afffundno abstractunlabeled
Modelling relational statistics with Bayes Nets
Oliver Schulte, Hassan Khosravi, Arthur E. Kirkpatrick, Tianxiang Gao, Yuke Zhu
2013· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
29
citations

How this was built: Screen · Findings · About