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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
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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 2 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.

affno abstractunlabeled
Parallel Algorithm Configuration
Frank Hutter, Holger H. Hoos, Kevin Leyton‐Brown
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
74
citations
affunlabeled
Efficient Inference of Optimal Decision Trees
Florent Avellaneda
2020· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
affunlabeled
Prediction intervals with random forests
Marie-Hélène Roy, Denis Larocque
2019· article· en· Statistical Methods in Medical Research· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
affunlabeled
META-DES.Oracle
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
2017· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
50
citations
affunlabeled
Automating data science
Tijl De Bie, Luc De Raedt, José Hernández‐Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams
2022· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
Evaluation-as-a-Service for the Computational Sciences
Frank Hopfgartner, Allan Hanbury, Henning Müller, Ivan Eggel, Krisztian Balog, Torben Brodt +10 more
2018· article· en· Journal of Data and Information Quality· Computer Science
machine prediction:candidate · noneconsensus · none
44
citations
affunlabeled
JANOS: An Integrated Predictive and Prescriptive Modeling Framework
David Bergman, Teng Huang, Philip C. Brooks, Andrea Lodi, Arvind U. Raghunathan
2022· article· en· Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)· Computer Science
machine prediction:candidate · noneconsensus · none
43
citations
affunlabeled
On-Line Adaptative Curriculum Learning for GANs
Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joëlle Pineau +1 more
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
41
citations
affunlabeled
Warm Starting CMA-ES for Hyperparameter Optimization
Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto, Yoshihiko Ozaki, Masaki Onishi
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affno abstractunlabeled
Ensembles of label noise filters: a ranking approach
Luís P. F. Garcia, Ana Carolina Lorena, Stan Matwin, André C. P. L. F. de Carvalho
2016· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
afffundno abstractunlabeled
An Experimental Study of Adaptive Capping in irace
Leslie Pérez Cáceres, Manuel López‐Ibáñez, Holger H. Hoos, Thomas Stützle
2017· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
31
citations
fundno affno abstractunlabeled
LMAE: A large margin Auto-Encoders for classification
Weifeng Liu, Tengzhou Ma, Qiangsheng Xie, Dapeng Tao, Jun Cheng
2017· article· en· Signal Processing· Computer Science
machine prediction:candidate · noneconsensus · none
30
citations
affunlabeled
HyperNOMAD
Dounia Lakhmiri, Sébastien Le Digabel, Christophe Tribes
2021· article· en· ACM Transactions on Mathematical Software· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
afffundunlabeled
Fast Sparse Decision Tree Optimization via Reference Ensembles
Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin +1 more
2022· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
affunlabeled
Strong Optimal Classification Trees
Sina Aghaei, Andrés Gómez, Phebe Vayanos
2024· article· en· Operations Research· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affno abstractunlabeled
Combining heterogeneous classifiers via granular prototypes
Tien Thanh Nguyen, Mai Phuong Nguyen, Xuan Cuong Pham, Alan Wee‐Chung Liew, Witold Pedrycz
2018· article· en· Applied Soft Computing· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations

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