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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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Machine Learning and ELM
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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
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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.

214 results · 1 filter active ·
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20082025
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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.
214 works in the cohort · of 4,299,418page 1 of 5

Labels cover 1 of 214 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 214 of 214 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
Extreme Learning Machines [Trends & Controversies]
Erik Cambria, Guang-Bin Huang, Hongming Zhou, Chi‐Man Vong, Jiarun Lin, Jianping Yin +24 more
2013· article· en· IEEE Intelligent Systems· Computer Science
machine prediction:candidate · noneconsensus · none
706
citations
afffundno abstractunlabeled
Extreme learning machine model for water network management
Ahmed M. A. Sattar, Ömer Faruk Ertuğrul, Bahram Gharabaghi, Edward A. McBean, Jiuwen Cao
2017· article· en· Neural Computing and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
146
citations
affno abstractunlabeled
Brain age prediction using improved twin SVR
M. A. Ganaie, M. Tanveer, Iman Beheshti
2022· article· en· Neural Computing and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
affno abstractunlabeled
Data Mining Methods for Modeling in Water Science
Seyedehelham Shirvani-Hosseini, Arvin Samadi-Koucheksaraee, Iman Ahmadianfar, Bahram Gharabaghi
2022· book-chapter· en· Studies in computational intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations
affunlabeled
Adaptive Consensus ADMM for Distributed Optimization
Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
2017· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations

How this was built: Screen · Findings · About