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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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Machine Learning and Data Classification
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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.

559 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
559 works in the cohort · of 4,299,418page 1 of 12

Labels cover 1 of 559 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 559 of 559 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.

fundno affunlabeled
Semi-Supervised Learning
Olivier Chapelle, Alexander Zien
2006· book· en· The MIT Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
4,308
citations
fundno affunlabeled
Automated Machine Learning
Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
2019· book· en· ˜The œSpringer series on challenges in machine learning· Computer Science
machine prediction:candidate · noneconsensus · none
1,396
citations
affunlabeled
Auto-WEKA
Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton‐Brown
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1,321
citations
affunlabeled
Density Estimation Using Real NVP
Laurent Dinh
2016· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
793
citations
affunlabeled
Evaluating Learning Algorithms
Nathalie Japkowicz, Mohak Shah
2011· book· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
753
citations
affunlabeled
Maxout Networks
Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
607
citations
affunlabeled
Multi-Task Bayesian Optimization
Kevin Swersky, Jasper Snoek, Ryan P. Adams
2013· article· en· Digital Access to Scholarship at Harvard (DASH) (Harvard University)· Computer Science
machine prediction:candidate · noneconsensus · none
452
citations
affunlabeled
Maxout Networks
Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio
2013· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
451
citations
afffundunlabeled
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram +2 more
2015· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
440
citations
affunlabeled
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram +2 more
2015· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
376
citations
affunlabeled
Boosting Neural Networks
Holger Schwenk, Yoshua Bengio
2000· article· en· Neural Computation· Computer Science
machine prediction:candidate · noneconsensus · none
303
citations
affunlabeled
Learning Feature Engineering for Classification
Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B. Khalil, Deepak S. Turaga
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
282
citations
affunlabeled
MixUp as Locally Linear Out-of-Manifold Regularization
Hongyu Guo, Yongyi Mao, Richong Zhang
2019· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
265
citations
affunlabeled
ActiveClean
Sanjay Krishnan, Jiannan Wang, Eugene Wu, Michael J. Franklin, Ken Goldberg
2016· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
244
citations
affunlabeled
Programming by optimization
Holger H. Hoos
2012· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
226
citations
affno abstractunlabeled
Lectures on the Nearest Neighbor Method
Gérard Biau, Luc Devroye
2015· book· en· Springer series in the data sciences· Computer Science
machine prediction:candidate · noneconsensus · none
224
citations
afffundno abstractunlabeled
ASlib: A benchmark library for algorithm selection
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette +5 more
2016· article· en· Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
213
citations
affunlabeled
Towards Robust Pattern Recognition: A Review
Xu-Yao Zhang, Cheng‐Lin Liu, Ching Y. Suen
2020· review· en· Proceedings of the IEEE· Computer Science
machine prediction:candidate · noneconsensus · none
153
citations
affunlabeled
Are random forests truly the best classifiers
Michael Wainberg, Babak Alipanahi, Brendan J. Frey
2016· article· en· Journal of Machine Learning Research· Computer Science
machine prediction:candidate · noneconsensus · none
135
citations
fundno affunlabeled
84 Automated machine learning
2020· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
96
citations
affunlabeled
The Principles of Data-Centric AI
Mohammad Hossein Jarrahi, Ali Memariani, Shion Guha
2023· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
91
citations

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