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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
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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.

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

affno abstractunlabeled
Bounding information leakage in machine learning
Ganesh Del Grosso, Georg Pichler, Catuscia Palamidessi, Pablo Piantanida
2023· article· en· Neurocomputing· Computer Science
machine prediction:candidate · noneconsensus · none
12
citations
affunlabeled
Perception-Guided Jailbreak Against Text-to-Image Models
Yihao Huang, Liang Le, Tianlin Li, Xiaojun Jia, Run Wang, Weikai Miao +2 more
2025· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Defending Adversarial Attacks via Semantic Feature Manipulation
Shuo Wang, ‪Surya Nepal‬, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen +1 more
2021· article· en· IEEE Transactions on Services Computing· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affno abstractunlabeled
Toward safe AI
Andres Morales‐Forero, Samuel Bassetto, Éric Coatanéa
2022· article· it· AI & Society· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
An Empirical Study of Testing Machine Learning in the Wild
Moses Openja, Foutse Khomh, Armstrong Foundjem, Zhen Ming Jiang, Mouna Abidi, Ahmed E. Hassan
2024· article· en· ACM Transactions on Software Engineering and Methodology· Computer Science
machine prediction:candidate · metaresearchconsensus · none
9
citations
affunlabeled
Manipulation Attacks on Learned Image Compression
Kang Liu, Yangyu Wu, Dan Feng, Benjamin Tan, Siddharth Garg
2023· article· en· IEEE Transactions on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
Antonio Emanuele Ciná, Jérôme Rony, Maura Pintor, Luca Demetrio, Ambra Demontis, Battista Biggio +2 more
2025· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations

How this was built: Screen · Findings · About