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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 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.

2,372 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.
2,372 works in the cohort · of 4,299,418page 6 of 48

Labels cover 1 of 2,372 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 2,372 of 2,372 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
Convolutional Neural Network
Reda Alhajj, Jon Rokne
2018· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Higher Order Neural Networks
Madan M. Gupta, Noriyasu Homma, Zeng-Guang Hou, Ashu M. G. Solo, Ivo Bukovský
2010· book-chapter· en· IGI Global eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affno abstractunlabeled
The Graph Neural Network Model
William L. Hamilton
2020· book-chapter· en· Synthesis lectures on artificial intelligence and machine learning· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affno abstractunlabeled
Algorithms and Data Structures
Frank Dehne, Jörg-Rüdiger Sack, Ulrike Stege
2015· book· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
Modular neural network architectures for classification
G. Auda, Mohamed S. Kamel, Hazem Raafat
2002· article· en· Proceedings of International Conference on Neural Networks (ICNN'96)· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
A NEW RBF NEURAL NETWORK FOR PREDICTION IN INDUSTRIAL CONTROL
Achraf Jabeur Telmoudi, Hatem Tlijani, Lotfi Nabli, Maaruf Ali, Radhi Mhiri
2012· article· en· International Journal of Information Technology & Decision Making· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
Radar vision
S. Haykin
2002· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affno abstractunlabeled
Neural networks primer
Alexander Derry, Martin Krzywinski, Naomi Altman
2023· article· es· Nature Methods· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations
affunlabeled
Robust training of microwave neural models
V. Devabhaktuni, Changgeng Xi, Fang Wang, Qi‐Jun Zhang
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations

How this was built: Screen · Findings · About