{"id":"W7160654328","doi":"10.1109/icscss64956.2025.11501145","title":"Next-Gen Access Control with AI-Powered Behavioral Defense","year":2025,"lang":"","type":"article","venue":"","topic":"Access Control and Trust","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Control (management); Access control; Key (lock); Action (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005824371,0.0005197289,0.0008149438,0.0002450916,0.001473987,0.002493197,0.001670644,0.0004131085,0.00514206],"category_scores_gemma":[0.0000750094,0.0004047258,0.0002825788,0.001486557,0.001024583,0.00249637,0.0002619936,0.0005109367,0.0001890308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001831468,"about_ca_system_score_gemma":0.001326653,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01510426,"about_ca_topic_score_gemma":0.01452122,"domain_scores_codex":[0.9960384,0.0004024968,0.0006729955,0.0008913394,0.0008045149,0.001190225],"domain_scores_gemma":[0.9979428,0.0002601114,0.0002638465,0.0005866108,0.0005376029,0.0004090287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003640428,0.002539997,0.5458175,0.0001182817,0.001371424,0.0003001895,0.006207928,0.0001210128,0.0002499814,0.2928971,0.01101418,0.135722],"study_design_scores_gemma":[0.0878179,0.004097676,0.342255,0.001215213,0.01318303,0.00002502022,0.05683245,0.009411423,0.001888225,0.0467126,0.4271004,0.009461054],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1917561,0.003094091,0.01578196,0.0296823,0.003161908,0.003586123,0.0001149114,0.0004964086,0.7523262],"genre_scores_gemma":[0.9787395,0.0001498541,0.00007008165,0.005610565,0.0003116134,0.0001142409,0.000007099037,0.00002764519,0.01496942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7869834,"threshold_uncertainty_score":0.9998404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03010898928701566,"score_gpt":0.3530630115100842,"score_spread":0.3229540222230686,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}