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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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Domain Adaptation and Few-Shot Learning
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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
fundfunder
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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.

705 results · 1 filter active ·
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20012025
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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.
705 works in the cohort · of 4,299,418page 6 of 15

Labels cover 0 of 705 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 705 of 705 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Loss of Plasticity in Deep Continual Learning
Shibhansh Dohare, Juan Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood
2023· preprint· en· Research Square· Computer Science
distilled prediction:candidate · research_integrityconsensus · none
7
citations
fundno affunlabeled
A Hybrid Loss for Multiclass and Structured Prediction
Qinfeng Shi, Mark D. Reid, Tibério S. Caetano, Anton van den Hengel, Zhenhua Wang
2014· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
afffundunlabeled
Domain Generalization by Rejecting Extreme Augmentations
Masih Aminbeidokhti, Fidel A. Guerrero Peña, Heitor R. Medeiros, Thomas Dubail, Éric Granger, Marco Pedersoli
2024· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Boosting with incomplete information
Gholamreza Haffari, Yang Wang, Shaojun Wang, Greg Mori
2008· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
7
citations
affno abstractunlabeled
Diffusion Transport Alignment
Andrés Orozco‐Duque, Guy Wolf, Kevin R. Moon
2023· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
7
citations
affunlabeled
Asymptotic theory of in-context learning by linear attention
Yue M. Lu, Mary I. Letey, Jacob A. Zavatone-Veth, Anindita Maiti, Cengiz Pehlevan
2025· article· en· Proceedings of the National Academy of Sciences· Computer Science
distilled prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Selective embedding for deep learning
Mert Sehri, Zehui Hua, Francisco de Assis Boldt, Patrick Dumond
2025· article· en· Knowledge-Based Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Domain Generalization via Semi-supervised Meta Learning
Hossein Sharifi-Noghabi, Hossein Asghari, Nazanin Mehrasa, Martin Ester
2020· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
6
citations
affunlabeled
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data
Amin Banitalebi-Dehkordi, Pratik Gujjar, Yong Zhang
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
6
citations

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