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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.

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.

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 7 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
Online Learning
Shai Shalev‐Shwartz, Shai Ben-David
2014· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
afffundunlabeled
DNAi: an open-source AI tool for unbiased DNA fiber analysis
Clément Playout, Yosra Mehrjoo, Renaud Duval, Marie Carole Boucher, Santiago Costantino, Hugo Würtele
2025· article· en· Nucleic Acids Research· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Conclusion
Nathalie Japkowicz, Mohak Shah
2011· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
Class Adaptive Network Calibration
Bingyuan Liu, Jérôme Rony, Adrián Galdrán, José Dolz, Ismail Ben Ayed
2022· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Profile Similarity
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Information Preserving Dimensionality Reduction
Shrinu Kushagra, Shai Ben-David
2015· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Federated Learning with Local Openset Noisy Labels
Zonglin Di, Zhaowei Zhu, Xiaoxiao Li, Yang Liu
2024· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Whole Page Unbiased Learning to Rank
Haitao Mao, Lixin Zou, Yujia Zheng, Jiliang Tang, Xiaokai Chu, Jiashu Zhao +2 more
2024· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
On-Line Adaptative Curriculum Learning for GANs
Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joëlle Pineau +1 more
2019· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Precision-based Boosting
Mohammad Hossein Nikravan, Marjan Movahedan, Sandra Zilles
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Imbalanced regression pipeline recommendation
Juscimara Gomes Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz
2025· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Deep Associative Classifier
Md. Rayhan Kabir, Samridhi Vaid, Nitakshi Sood, Osmar R. Zai͏̈ane
2022· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Classification Methods
Aijun An
2011· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Iterative Teaching by Label Synthesis
Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller
2021· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
On Lower Bounding Minimal Model Count
Mohimenul Kabir, Kuldeep S. Meel
2024· article· en· Theory and Practice of Logic Programming· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Learning from Ambiguous Data with Hard Labels
Zheng He, Nan Lu, Lichen Bai, Bao Li, Shuo Yang, Mingming Sun +1 more
2025· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Machine Learning
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
Semi-supervised learning from coarse histopathology labels
Fahimeh Fooladgar, Minh Nguyen Nhat To, Golara Javadi, Samira Sojoudi, Walid Eshumani, Silvia D. Chang +3 more
2022· article· en· Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

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