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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 11 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.

affno abstractunlabeled
DeepFlux for Skeleton Detection in the Wild
Yongchao Xu, Yukang Wang, Stavros Tsogkas, Jianqiang Wan, Xiang Bai, Sven Dickinson +1 more
2021· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
YaConv: Convolution with Low Cache Footprint
Ivan Korostelev, João P. L. de Carvalho, José E. Moreira, José Nelson Amaral
2022· article· en· ACM Transactions on Architecture and Code Optimization· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
RGB-LiDAR fusion for accurate 2D and 3D object detection
Morteza Mousa-Pasandi, Tianran Liu, Yahya Massoud, Robert Laganière
2023· article· en· Machine Vision and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
Local Submodularization for Binary Pairwise Energies
Lena Gorelick, Yuri Boykov, Olga Veksler, Ismail Ben Ayed, Andrew Delong
2016· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
afffundunlabeled
T-RecX
Nikhil Pratap Ghanathe, Steven J. E. Wilton
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
fundno affunlabeled
High performance distributed deep learning
Dhabaleswar K. Panda, Ammar Ahmad Awan, Hari Subramoni
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
aboutno affunlabeled
Balanced Decoupled Spatial Convolution for CNNs
Guotian Xie, Kuiyuan Yang, Ting Zhang, Jingdong Wang, Jianhuang Lai
2019· article· en· IEEE Transactions on Neural Networks and Learning Systems· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Object detection in aerial images using DOTA dataset: A survey
Ziyi Chen, Huayou Wang, Xinyuan Wu, Jing Wang, Xinrui Lin, Cheng Wang +3 more
2024· article· en· International Journal of Applied Earth Observation and Geoinformation· Computer Science
machine prediction:candidate · noneconsensus · none
8
citations

How this was built: Screen · Findings · About