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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 1 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.

afffundunlabeled
Scene Parsing through ADE20K Dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, Antonio Torralba
2017· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3,165
citations
affunlabeled
Object Detection in 20 Years: A Survey
Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, Jieping Ye
2023· article· en· Proceedings of the IEEE· Computer Science
machine prediction:candidate · noneconsensus · none
2,868
citations
affunlabeled
Deep Learning for Generic Object Detection: A Survey
Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu +1 more
2019· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
2,776
citations
afffundno abstractunlabeled
Semantic Understanding of Scenes Through the ADE20K Dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso +1 more
2018· article· en· International Journal of Computer Vision· Computer Science
machine prediction:candidate · noneconsensus · none
1,667
citations
afffundunlabeled
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Ferdinand Christ, Hongwei Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis +103 more
2022· article· en· Medical Image Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
1,164
citations
affunlabeled
Deep Pyramidal Residual Networks
Dongyoon Han, Jiwhan Kim, Junmo Kim
2017· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
687
citations
affunlabeled
Cnvlutin
Jorge Albericio, Patrick Judd, Tayler Hetherington, Tor M. Aamodt, Natalie Enright Jerger, Andreas Moshovos
2016· article· en· ACM SIGARCH Computer Architecture News· Computer Science
machine prediction:candidate · noneconsensus · none
639
citations
affno abstractunlabeled
Loss odyssey in medical image segmentation
Jun Ma, Jianan Chen, Matthew Ng, Yu Li, Chen Li, Xiaoping Yang +1 more
2021· review· en· Medical Image Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
618
citations
affunlabeled
UPSNet: A Unified Panoptic Segmentation Network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer +1 more
2019· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
422
citations
afffundunlabeled
Stripes: Bit-serial deep neural network computing
Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor M. Aamodt, Andreas Moshovos
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
329
citations
afffundno abstractunlabeled
Constrained-CNN losses for weakly supervised segmentation
Hoel Kervadec, José Dolz, Meng Tang, Éric Granger, Yuri Boykov, Ismail Ben Ayed
2019· article· en· Medical Image Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
282
citations

How this was built: Screen · Findings · About