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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.

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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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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.

206 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.
206 works in the cohort · of 4,299,418page 4 of 5

Labels cover 0 of 206 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 206 of 206 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Analysis of the IJCNN 2011 UTL challenge
Isabelle Guyon, Gideon Dror, Vincent Lemaire, Daniel Silver, Graham W. Taylor, David W. Aha
2012· article· en· Neural Networks· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
fundno affno abstractunlabeled
Chaos-based reinforcement learning with TD3
Toshitaka Matsuki, Yusuke Sakemi, Kazuyuki Aihara
2025· article· en· Neural Networks· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
afffundno abstractunlabeled
LAGO on the unit sphere
Alexandra Laflamme-Sanders, Mu Zhu
2008· article· en· Neural Networks· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
fundno affno abstractunlabeled
Echo state networks are universal
Lyudmila Grigoryeva, Juan‐Pablo Ortega
2018· preprint· en· Neural Networks· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+open_science+research_integrityconsensus · none
1
citations
afffundno abstractunlabeled
Rethinking softmax in incremental learning
Zheng Zhai, Jiali Zhang, Haiyu Wang, Mingxin Wu, Keshun Yang, Xiaoyan Qiao +1 more
2025· article· en· Neural Networks· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
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How this was built: Screen · Findings · About