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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
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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
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 5 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
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
affunlabeled
The bias hunter
Douglas Starr
2022· article· en· Science· Computer Science
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Attack and Defense on Aircraft Trajectory Prediction Algorithms
Quincy G. van Iersel, Alejandro Murrieta Mendoza, Roberto S. Felix Patron, Seyed Mohammad Hashemi, Ruxandra Mihaela Botez
2022· article· en· AIAA AVIATION 2022 Forum· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
fundno affgemma · no categorygpt · no categorymodels split
ARCH-COMP 2024 Category Report: Falsification
Tanmay Khandait, Federico Formica, Paolo Arcaini, Surdeep Chotaliya, Georgios Fainekos, Abdelrahman Hekal +13 more
2024· article· en· EPiC series in computing· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
fundno affunlabeled
War-Algorithm Accountability
Dustin A. Lewis, Naz K. Modirzadeh, Gabriella Blum
2016· preprint· en· Computer Science
machine prediction:candidate · stsconsensus · none
7
citations
fundno affunlabeled
RAILS: A Robust Adversarial Immune-Inspired Learning System
Ren Wang, Tianqi Chen, Stephen Lindsly, Cooper Stansbury, Alnawaz Rehemtulla, Indika Rajapakse +1 more
2022· article· en· IEEE Access· Computer Science
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
SOAR: Second-Order Adversarial Regularization
Avery Ma, Fartash Faghri, Nicolas Papernot, Amir‐massoud Farahmand
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
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How this was built: Screen · Findings · About