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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
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
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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 13 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Cascade Adversarial Attack Search
Ziwen Wang, Xiangkun Sun, Shangshang Yang, Xiaoshan Yu, Ye Tian
2025· article· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · insufficient_payload
0
citations
affno abstractunlabeled
Trust Region Sequential Variational Inference
Geon-Hyeong Kim, Youngsoo Jang, Jongmin Lee, Wonseok Jeon, Hongseok Yang, Kee-Eung Kim
2019· article· en· Asian Conference on Machine Learning· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Feasibility of Adversarial Attacks Against Machine Learning Models
Kimia Tahayori, Sherif Saad, Mohammad Mamun, Saeed Samet
2024· book-chapter· en· Lecture notes on data engineering and communications technologies· Computer Science
distilled prediction:candidate · metaepi_narrow+open_science+research_integrityconsensus · open_science
0
citations
aboutno affunlabeled
Verification Witnesses from Verification Tools (SV-COMP 2022)
Dirk Beyer
2022· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
distilled prediction:candidate · metaepi_narrow+sts+scholarly_communication+open_science+insufficient_payloadconsensus · insufficient_payload
0
citations
venueno affno abstractunlabeled
Data Contamination in AI Evaluation
Alaeddin Acar
2025· article· en· JMIR Medical Informatics· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Combining Machine Learning Defenses without Conflicts
Vasisht Duddu, Rui Zhang, N. Asokan
2024· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrow+open_science+research_integrityconsensus · none
0
citations
afffundaboutunlabeled
NORAD : Remaining Relevant
Michael Dawson
2019· article· en· The School of Public Policy Publications· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
0
citations
affunlabeled
Backdoors in Code Summarizers: How Bad Is It?
Chenyu Wang, Zhou Yang, Yaniv Harel, David Lo
2025· article· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Adversarial Machine Learning (AML)
Nicolas Papernot
2025· book-chapter· en· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
0
citations

How this was built: Screen · Findings · About