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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 10 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
MAPLE: Microprocessor A Priori for Latency Estimation
Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
MAPLE-Edge: A Runtime Latency Predictor for Edge Devices
Saeejith Nair, Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
afffundunlabeled
Multi-Modal Streaming 3D Object Detection
Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang, Rabab Ward
2023· article· en· IEEE Robotics and Automation Letters· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Hybrid Models for Learning to Branch
Prateek Gupta, Maxime Gasse, Elias B. Khalil, Pawan Kumar Mudigonda, Andrea Lodi, Yoshua Bengio
2020· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
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
Sparse Weight Activation Training
Aamir Raihan, Tor M. Aamodt
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations

How this was built: Screen · Findings · About