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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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Advances in Data Analysis and 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.

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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.

40 results · 1 filter active ·
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20092025
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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.
40 works in the cohort · of 4,299,418page 1 of 1

Labels cover 0 of 40 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 40 of 40 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
A LASSO-penalized BIC for mixture model selection
Sakyajit Bhattacharya, Paul D. McNicholas
2013· article· en· Advances in Data Analysis and Classification· Computer Science
machine prediction:candidate · noneconsensus · none
58
citations
affno abstractunlabeled
Mixture model averaging for clustering
2014· article· en· Advances in Data Analysis and Classification· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
Regularized fuzzy clusterwise ridge regression
Hye Won Suk, Heungsun Hwang
2009· article· en· Advances in Data Analysis and Classification· Mathematics
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Parsimonious cluster systems
François Brucker, Alain Gély
2009· article· en· Advances in Data Analysis and Classification· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
Functional fuzzy clusterwise regression analysis
Tianyu Tan, Hye Won Suk, Heungsun Hwang, Jooseop Lim
2013· article· en· Advances in Data Analysis and Classification· Mathematics
machine prediction:candidate · noneconsensus · none
6
citations
afffundno abstractunlabeled
Clustering functional data via variational inference
Chengqian Xian, Camila P. E. de Souza, John Jewell, Ronaldo Dias
2024· article· en· Advances in Data Analysis and Classification· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundno abstractunlabeled
Determinantal consensus clustering
Serge Vicente, Alejandro Murua
2022· article· en· Advances in Data Analysis and Classification· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
afffundno abstractunlabeled
Clustering discrete-valued time series
Tyler Roick, Dimitris Karlis, Paul D. McNicholas
2020· preprint· en· Advances in Data Analysis and Classification· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

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