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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.

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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 6 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.

affunlabeled
Sequential model-based ensemble optimization
Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
Detecting Noisy Labels with Repeated Cross-Validations
Jianan Chen, Vishwesh Ramanathan, Tony Xu, Anne L. Martel
2024· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Parallel Predictor Generation
David B. Skillicorn
2002· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Evaluating Instance Generators by Configuration
Sam Bayless, Dave A. D. Tompkins, Holger H. Hoos
2014· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
K-Means
Christo El Morr, Manar Jammal, Hossam Ali‐Hassan, Walid El-Hallak
2022· book-chapter· en· International series in management science/operations research/International series in operations research & management science· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Minimum consistent subset of simple graph classes
Sanjana Dey, Anil Maheshwari, Subhas C. Nandy
2023· article· en· Discrete Applied Mathematics· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
SELF-INFORMED NEURAL NETWORK STRUCTURE LEARNING
David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
2015· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
Yuchen Jin, Tianyi Zhou, Liangyu Zhao, Yibo Zhu, Chuanxiong Guo, Marco Canini +1 more
2021· preprint· en· King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Minimum Consistent Subset Problem for Trees
Sanjana Dey, Anil Maheshwari, Subhas C. Nandy
2021· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
MOAZ: A Multi-Objective AutoML-Zero Framework
Ritam Guha, Wei Ao, Stephen Kelly, Vishnu Naresh Boddeti, Erik D. Goodman, Wolfgang Banzhaf +1 more
2023· article· en· Proceedings of the Genetic and Evolutionary Computation Conference· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Principal Sample Analysis for Data Ranking
Benyamin Ghojogh
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
aboutno affno abstractunlabeled
Learning Evaluation for Intelligence
Jyotismita Talukdar, Thipendra P. Singh, Basanta Barman
2023· book-chapter· en· Advanced technologies and societal change· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
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