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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.

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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
Evidence source
Study design
Label agreement
Label status

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
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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.

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 3 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
DIBS: Diversity Inducing Information Bottleneck in Model Ensembles
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
DisGUIDE: Disagreement-Guided Data-Free Model Extraction
Jonathan Rosenthal, Eric Enouen, Hung Viet Pham, Lin Tan
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Reinterpreting Importance-Weighted Autoencoders
Chris Cremer, Quaid Morris, David Duvenaud
2017· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Low Frequency Adversarial Perturbation
Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
2018· article· en· Uncertainty in Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations
affunlabeled
Revisiting the Importance of Amplifying Bias for Debiasing
Jungsoo Lee, Jeong-Hoon Park, Daeyoung Kim, Juyoung Lee, Edward Choi, Jaegul Choo
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affno abstractunlabeled
Improving ML Safety with Partial Specifications
Rick Salay, Krzysztof Czarnecki
2019· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Machine learning robustness: a primer
Houssem Ben Braiek, Foutse Khomh
2025· book-chapter· en· Elsevier eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
14
citations
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