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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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Data Stream Mining Techniques
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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.

322 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.
322 works in the cohort · of 4,299,418page 1 of 7

Labels cover 0 of 322 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 322 of 322 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
StreamKM++
Marcel R. Ackermann, Marcus Märtens, Christoph Raupach, Kamil Swierkot, Christiane Lammersen, Christian Sohler
2012· article· en· ACM Journal of Experimental Algorithmics· Computer Science
machine prediction:candidate · noneconsensus · none
295
citations
affunlabeled
On Distributed Fuzzy Decision Trees for Big Data
Armando Segatori, Francesco Marcelloni, Witold Pedrycz
2017· article· en· IEEE Transactions on Fuzzy Systems· Computer Science
machine prediction:candidate · noneconsensus · none
145
citations
affno abstractunlabeled
Adapting dynamic classifier selection for concept drift
Paulo Ricardo Lisboa de Almeida, Luiz S. Oliveira, Alceu S. Britto, Robert Sabourin
2018· article· en· Expert Systems with Applications· Computer Science
machine prediction:candidate · noneconsensus · none
84
citations
affunlabeled
Cardinality estimation using neural networks
Henry Liu, Mingbin Xu, Ziting Yu, Vincent Corvinelli, Calisto Zuzarte
2015· article· en· Computer Science and Software Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
57
citations
fundno affno abstractunlabeled
Clustering Text Data Streams
Yubao Liu, Jiarong Cai, Jian Yin, Ada Wai-Chee Fu
2008· article· en· Journal of Computer Science and Technology· Computer Science
machine prediction:candidate · noneconsensus · none
57
citations
affunlabeled
Streaming Random Forests
Hanady M. Abdulsalam, David B. Skillicorn, Patrick Martin
2007· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
40
citations
affunlabeled
Consistency of Online Random Forests
Misha Denil, David S. Matheson
2013· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
37
citations
affunlabeled
Users, Programmers, and Statistical Software
John M. Chambers
2000· article· en· Journal of Computational and Graphical Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
Launch and Iterate: Reducing Prediction Churn
Mahdi Milani Fard, Quentin Cormier, Kevin Robert Canini, Maya R. Gupta
2016· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
23
citations

How this was built: Screen · Findings · About