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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.

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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 5 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. 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
Requirements for Machine Lifelong Learning
Daniel Silver, Ryan Poirier
2007· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Unifying Top–Down Views by Task-Specific Domain Adaptation
Jianzhe Lin, Tianze Yu, Lichao Mou, Xiao Xiang Zhu, Rabab Ward, Z. Jane Wang
2020· article· en· IEEE Transactions on Geoscience and Remote Sensing· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Minimax and Neyman-Pearson meta-learning for outlier languages
Edoardo Maria Ponti, Rahul Aralikatte, Disha Shrivastava, Siva Reddy, Anders Søgaard
2021· article· en· Edinburgh Research Explorer (University of Edinburgh)· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Few-Shot Domain Adaptation with Polymorphic Transformers
Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng +4 more
2021· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affno abstractunlabeled
Continual Learning with Dual Regularizations
Xuejun Han, Yuhong Guo
2021· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
Center transfer for supervised domain adaptation
Xiuyu Huang, Nan Zhou, Jian Huang, Huaidong Zhang, Witold Pedrycz, Kup‐Sze Choi
2023· article· en· Applied Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Re-basin via implicit Sinkhorn differentiation
Fidel A. Guerrero Peña, Heitor R. Medeiros, Thomas Dubail, Masih Aminbeidokhti, Éric Granger, Marco Pedersoli
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Auto-Tuning Kernel Mean Matching
Yun-Qian Miao, Ahmed Farahat, Mohamed S. Kamel
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
LogDet Metric-Based Domain Adaptation
Youfa Liu, Bo Du, Weiping Tu, Mingming Gong, Yuhong Guo, Dacheng Tao
2020· article· en· IEEE Transactions on Neural Networks and Learning Systems· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Transfer Incremental Learning Using Data Augmentation
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia, Matthieu Arzel, Michel Jézéquel
2018· article· en· Applied Sciences· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Distilled Meta-learning for Multi-Class Incremental Learning
Hao Liu, Zhaoyu Yan, Bing Liu, Jiaqi Zhao, Yong Zhou, Abdulmotaleb El Saddik
2023· article· en· ACM Transactions on Multimedia Computing Communications and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About