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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 Mining and Knowledge Discovery
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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.

49 results · 1 filter active ·
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20032025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
49 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 49 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 49 of 49 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
Mining functional dependencies from data
Hong Yao, Howard J. Hamilton
2007· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
90
citations
affno abstractunlabeled
Data Clustering with Partial Supervision
Abdelhamid Bouchachia, Witold Pedrycz
2006· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
75
citations
affno abstractunlabeled
Mining outlying aspects on numeric data
Lei Duan, Guanting Tang, Jian Pei, James Bailey, Akiko Campbell, Changjie Tang
2015· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
70
citations
affno abstractunlabeled
Privacy-preserving boosting
Sébastien Gambs, Balázs Kégl, Esma Aı̈meur
2007· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
60
citations
affno abstractunlabeled
Publishing anonymous survey rating data
Xiaoxun Sun, Hua Wang, Jiuyong Li, Jian Pei
2010· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · metaresearchconsensus · none
56
citations
affno abstractunlabeled
Extreme-value-theoretic estimation of local intrinsic dimensionality
Laurent Amsaleg, Oussama Chelly, Teddy Furon, Stéphane Girard, Michael E. Houle, Ken‐ichi Kawarabayashi +1 more
2018· article· en· Data Mining and Knowledge Discovery· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
52
citations
affno abstractunlabeled
Ensembles of label noise filters: a ranking approach
Luís P. F. Garcia, Ana Carolina Lorena, Stan Matwin, André C. P. L. F. de Carvalho
2016· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affno abstractunlabeled
Relational peculiarity-oriented mining
Muneaki Ohshima, Ning Zhong, Yiyu Yao, Chunnian Liu
2007· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Adjusting for scorekeeper bias in NBA box scores
Matthew van Bommel, Luke Bornn
2017· article· en· Data Mining and Knowledge Discovery· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
12
citations
afffundno abstractunlabeled
What distinguish one from its peers in social networks?
Yi‐Chen Lo, Jhao-Yin Li, Mi-Yen Yeh, Shou-De Lin, Jian Pei
2013· article· en· Data Mining and Knowledge Discovery· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Efficient outlier detection in numerical and categorical data
Eugênio F. Cabral, Braulio V. Sánchez Vinces, Guilherme D. F. Silva, Jörg Sander, Robson L. F. Cordeiro
2025· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Detecting and reacting to smart home novelties
Lawrence B. Holder, Baxter Eaves, Patrick Shafto, Christopher Pereyda, Brian Thomas, Diane J. Cook
2025· article· en· Data Mining and Knowledge Discovery· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations

How this was built: Screen · Findings · About