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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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Adversarial Robustness in Machine Learning
Retraction
Abstract
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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
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

797 results · 1 filter active ·
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20012025
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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.
797 works in the cohort · of 4,299,418page 11 of 16

Labels cover 2 of 797 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 797 of 797 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
Detecting Extrapolation with Local Ensembles
David Madras, James Atwood, Alex D’Amour
2019· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
Adversarial Attacks on Data Attribution
Xinhe Wang, Pingbang Hu, Junwei Deng, Jiaqi W. Ma
2024· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
NNoculation: Catching BadNets in the Wild
Akshaj Kumar Veldanda, Kang Liu, Benjamin Tan, P. Krishnamurthy, Farshad Khorrami, Ramesh Karri +2 more
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Stochastic Neural Network with Kronecker Flow.
Chin-Wei Huang, Abdelaziz Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville
2019· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
AI Security Challenges, Opportunities and Future Work
Dilli Prasad Sharma, Arash Habibi Lashkari, Mahdi Daghmehchi Firoozjaei, Samaneh Mahdavifar, Pulei Xiong
2025· book-chapter· en· Progress in IS· Computer Science
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
ROBUST MACHINE LEARNING USING SUPERQUANTILES
2021· dissertation· Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About