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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.

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.

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 1 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
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
Yanmin Sun, Andrew K. C. Wong, Mohamed S. Kamel
2009· review· en· International Journal of Pattern Recognition and Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
1,690
citations
affunlabeled
Class imbalances versus small disjuncts
Taeho Jo, Nathalie Japkowicz
2004· article· en· ACM SIGKDD Explorations Newsletter· Computer Science
machine prediction:candidate · noneconsensus · none
681
citations
affno abstractunlabeled
The class imbalance problem in deep learning
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Paula Branco, Bartosz Krawczyk, Nathalie Japkowicz
2022· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
270
citations
afffundaboutunlabeled
Explicitly representing expected cost
Chris Drummond, Robert C. Holte
2000· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
186
citations
afffundunlabeled
Test strategies for cost-sensitive decision trees
Charles X. Ling, Victor S. Sheng, Qiang Yang
2006· article· en· IEEE Transactions on Knowledge and Data Engineering· Computer Science
machine prediction:candidate · noneconsensus · none
145
citations
affno abstractunlabeled
Overlap versus Imbalance
Misha Denil, Thomas Trappenberg
2010· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
134
citations
affno abstractunlabeled
The class imbalance problem
Fadel M. Megahed, Ying‐Ju Chen, Aly Megahed, Yuya Jeremy Ong, Naomi Altman, Martin Krzywinski
2021· article· en· Nature Methods· Computer Science
machine prediction:candidate · noneconsensus · none
126
citations
affno abstractunlabeled
Evaluation Methods for Ordinal Classification
Lisa Gaudette, Nathalie Japkowicz
2009· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
106
citations
affunlabeled
Satisfying Real-world Goals with Dataset Constraints
Gabriel Goh, Andrew Cotter, Maya R. Gupta, Michael P. Friedlander
2016· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
80
citations
affno abstractunlabeled
Uncertainty-aware credit card fraud detection using deep learning
Maryam Habibpour, Hassan Gharoun, Mohammadreza Mehdipour, AmirReza Tajally, Hamzeh Asgharnezhad, Afshar Shamsi +2 more
2023· article· en· Engineering Applications of Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
66
citations

How this was built: Screen · Findings · About