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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 7 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.

afffundno abstractunlabeled
Training Binarized Neural Networks Using MIP and CP
Rodrigo Toro Icarte, León Illanes, Margarita P. Castro, André A. Ciré, Sheila A. McIlraith, J. Christopher Beck
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
COMET
Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li +2 more
2021· article· en· Proceedings of the VLDB Endowment· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
afffundno abstractunlabeled
Proteus: Exploiting precision variability in deep neural networks
Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor M. Aamodt, Natalie Enright Jerger, Raquel Urtasun +1 more
2017· article· en· Parallel Computing· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
venueno affunlabeled
Object Detection Using Stacked YOLOv3
Sai Shilpa Padmanabula, Ramya Chowdary Puvvada, S. Venkatramaphanikumar, Venkata Krishna Kishore Kolli
2020· article· en· Ingénierie des systèmes d information· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Squeeze-and-Attention Networks for Semantic Segmentation
Zilong Zhong, Zhong Qiu Lin, Rene Bidart, Xiaodan Hu, Ibrahim Ben Daya, Zhifeng Li +3 more
2019· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations

How this was built: Screen · Findings · About