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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.

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 8 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
Can Go AIs Be Adversarially Robust?
Tom Tseng, Euan McLean, Kellin Pelrine, Tony Tong Wang, Adam Gleave
2025· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
HUDD: A tool to debug DNNs for safety analysis
Hazem Fahmy, Fabrizio Pastore, Lionel Briand
2022· article· en· 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
InfoCensor
Tianhang Zheng, Baochun Li
2022· article· en· Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Toward Stronger Textual Attack Detectors
Pierre Colombo, Marine Picot, Nathan Noiry, Guillaume Staerman, Pablo Piantanida
2023· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Spartan Networks
François Menet, Paul Berthier, Michel Gagnon, José M. Fernandez
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
afffundunlabeled
HUDD
Hazem Fahmy, Fabrizio Pastore, Lionel Briand
2022· preprint· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
3
citations
affno abstractunlabeled
Improving Adversarial Transferability via Model Alignment
Avery Ma, Amir‐massoud Farahmand, Yangchen Pan, Philip Torr, Jindong Gu
2024· book-chapter· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Soft Adversarial Training Can Retain Natural Accuracy
Abhijith Sharma, Apurva Narayan
2022· article· en· Proceedings of the 14th International Conference on Agents and Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Protecting image privacy through adversarial perturbation
Baoyu Liang, Chao Tong, Chao Lang, Qinglong Wang, Joel J. P. C. Rodrigues, S. A. Kozlov
2021· article· en· Multimedia Tools and Applications· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About