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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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Imbalanced Data Classification 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.

529 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.
529 works in the cohort · of 4,299,418page 2 of 11

Labels cover 1 of 529 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 529 of 529 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
Decision tree with better ranking
Charles X. Ling, Robert Yan
2003· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
57
citations
affunlabeled
Satisfying real-world goals with dataset constraints
Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander
2016· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
55
citations
affno abstractunlabeled
Class Imbalance Problem
Charles X. Ling, Victor S. Sheng
2017· book-chapter· en· Encyclopedia of Machine Learning and Data Mining· Computer Science
machine prediction:candidate · noneconsensus · none
42
citations
affno abstractunlabeled
Hybrid Cost-Sensitive Decision Tree
Shengli Sheng, Charles X. Ling
2005· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
35
citations
affunlabeled
Cost-sensitive test strategies
Victor S. Sheng, Charles X. Ling, Ailing Ni, Shichao Zhang
2006· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
33
citations
affunlabeled
Towards a Consistent Interpretation of AIOps Models
Yingzhe Lyu, Gopi Krishnan Rajbahadur, Dayi Lin, Boyuan Chen, Zhen Ming Jiang
2021· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · noneconsensus · none
28
citations
afffundno abstractunlabeled
Evaluating Misclassifications in Imbalanced Data
William Elazmeh, Nathalie Japkowicz, Stan Matwin
2006· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
22
citations

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