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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 ELM
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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.

214 results · 1 filter active ·
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20082025
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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.
214 works in the cohort · of 4,299,418page 3 of 5

Labels cover 1 of 214 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 214 of 214 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
Applying Least Angle Regression to ELM
Hang Shao, Nathalie Japkowicz
2012· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Granular transfer learning
Rami Al‐Hmouz, Witold Pedrycz, Medhat Awadallah, Ahmed Chiheb Ammari
2024· article· en· Neurocomputing· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
A Precise Performance Analysis of Support Vector Regression
Houssem Sifaou, Abla Kammoun, Mohamed‐Slim Alouini
2021· article· en· King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology)· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Proceedings of ELM-2015 Volume 2
Jiuwen Cao, Kezhi Mao, Q. M. Jonathan Wu, Amaury Lendasse
2016· book· en· Proceedings in adaptation, learning and optimization· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Matrix randomized autoencoder
Shichen Zhang, Tianlei Wang, Jiuwen Cao, Wandong Zhang, Badong Chen
2023· article· en· Pattern Recognition· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Random Projection
Benyamin Ghojogh, Mark Crowley, Fakhri Karray, Ali Ghodsi
2023· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Robust Asymmetric Learning in POMDPs
Andrew Warrington, Jonathan Lavington, Adam Ścibior, Mark Schmidt, Frank Wood
2021· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
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