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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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Neural Networks and Reservoir Computing
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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.

affaffiliation
fundfunder
venuejournal
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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.

516 results · 1 filter active ·
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20002025
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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.
516 works in the cohort · of 4,299,418page 10 of 11

Labels cover 0 of 516 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 516 of 516 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
Silicon Photonics for Training Deep Neural Networks
Bhavin J. Shastri, Matthew J. Filipovich, Zhimu Guo, Paul R. Prucnal, Sudip Shekhar, Volker J. Sorger
2022· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Neuromorphic Photonic Networks
Bhavin J. Shastri, Simon Bilodeau, Bicky A. Márquez, Alexander N. Tait, Thomas Ferreira de Lima, Chaoran Huang +3 more
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Optical neuromorphic processing based on Kerr microcombs
Xingyuan Xu, Mengxi Tan, Jiayang Wu, Andreas Boes, Bill Corcoran, Thach G. Nguyen +5 more
2021· article· en· Conference on Lasers and Electro-Optics· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Multicellular Reservoir Computing
Vladimir Nikolić, Moriah Echlin, Boris Aguilar, Ilya Shmulevich
2022· preprint· en· bioRxiv (Cold Spring Harbor Laboratory)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Reconfigurable unitary transformations of optical beam arrays
Aldo C. Martinez-Becerril, Siwei Luo, Jordan T. R. Pagé, Li Liu, Lambert Giner, Raphael A. Abrahão +1 more
2022· article· en· Frontiers in Optics + Laser Science 2022 (FIO, LS)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
The Search for Feedback in Reinforcement Learning
Ran Wang, Aayushman Sharma, Karthikeya S. Parunandi, Raman Goyal, Suman Chakravorty
2025· preprint· en· Journal of Dynamic Systems Measurement and Control· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
OSA Trends in Optics and Photonics
Siegfried Janz, M. Pearson, B. Lamontagne, L. Erickson, André Delage, Pavel Cheben +5 more
2002· article· en· NPARC· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About