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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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Advanced Neural Network Applications
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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.

1,336 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.
1,336 works in the cohort · of 4,299,418page 4 of 27

Labels cover 0 of 1,336 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 1,336 of 1,336 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
RBGNet: Ray-based Grouping for 3D Object Detection
Haiyang Wang, Shaoshuai Shi, Ze Yang, Rongyao Fang, Qian Qi, Hongsheng Li +2 more
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)· Computer Science
machine prediction:candidate · noneconsensus · none
65
citations
affunlabeled
Stripes: Bit-Serial Deep Neural Network Computing
Patrick Judd, Jorge Albericio, Andreas Moshovos
2016· article· en· IEEE Computer Architecture Letters· Computer Science
machine prediction:candidate · noneconsensus · none
62
citations
afffundno abstractunlabeled
3D U-Net for Brain Tumour Segmentation
Raghav Mehta, Tal Arbel
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affunlabeled
DeepFlux for Skeletons in the Wild
Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affno abstractunlabeled
Constraint-Aware Deep Neural Network Compression
Changan Chen, Frederick Tung, Naveen Vedula, Greg Mori
2018· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
59
citations
affunlabeled
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
Zhangfu Dong, Yuting He, Xiaoming Qi, Yang Chen, Huazhong Shu, Jean-Louis Coatrieux +2 more
2022· article· en· Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
57
citations
affunlabeled
Identifying Unknown Instances for Autonomous Driving
Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun
2019· article· en· Conference on Robot Learning· Computer Science
machine prediction:candidate · noneconsensus · none
57
citations
affunlabeled
Graph HyperNetworks for Neural Architecture Search.
Wenjun Zhang, Mengye Ren, Raquel Urtasun
2018· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
53
citations
venueno affunlabeled
ShipYOLO: An Enhanced Model for Ship Detection
Xu Han, Lining Zhao, Yue Ning, Jingfeng Hu
2021· article· en· Journal of Advanced Transportation· Computer Science
machine prediction:candidate · noneconsensus · none
52
citations

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