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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 Data Classification
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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.

559 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.
559 works in the cohort · of 4,299,418page 5 of 12

Labels cover 1 of 559 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 559 of 559 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.

fundno affunlabeled
Assisted design of data science pipelines
Sergey Redyuk, Zoi Kaoudi, Sebastian Schelter, Volker Markl
2024· article· en· The VLDB Journal· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundno abstractunlabeled
Generative neural architecture search
Xiaotong Zhai, Shu Li, Guoqiang Zhong, Tao Li, Fuchang Zhang, Rachid Hedjam
2025· article· en· Neurocomputing· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Dataset Evolver: An Interactive Feature Engineering Notebook
Fatemeh Nargesian, Udayan Khurana, Tejaswini Pedapati, Horst Samulowitz, Deepak S. Turaga
2018· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Sequential model-based ensemble optimization
Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Ensemble Squared: A Meta AutoML System
Jason J. Yoo, Tony Joseph, Dylan Yung, S. Ali Nasseri, Frank Wood
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
fundno affunlabeled
A Quest for AI Knowledge
Joshua Gans
2025· report· en· National Bureau of Economic Research· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
ASlib: A Benchmark Library for Algorithm Selection
Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette +5 more
2015· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Data Acquisition for Improving Model Confidence
Yifan Li, Xiaohui Yu, Nick Koudas
2024· article· en· Proceedings of the ACM on Management of Data· Computer Science
machine prediction:candidate · noneconsensus · none
4
citations

How this was built: Screen · Findings · About