{"id":"W4413823650","doi":"10.1109/icccn65249.2025.11133927","title":"Protection of Critical Emergency Response Infrastructures through Machine Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emergency response; Disaster response; Computer science; Critical infrastructure; Crisis response; Emergency management; Computer security; Medical emergency; Political science; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009359524,0.0007328728,0.0005255555,0.001185797,0.0003443837,0.001005615,0.0009484189,0.0007769768,0.0007233904],"category_scores_gemma":[0.003307851,0.000272083,0.0006494642,0.0006785945,0.0004908763,0.001196102,0.0008192194,0.00103134,0.0004305889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007067062,"about_ca_system_score_gemma":0.00101766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003463861,"about_ca_topic_score_gemma":0.002388998,"domain_scores_codex":[0.9994544,0.0001373427,0.00003830392,0.0001470695,0.0001376986,0.00008513548],"domain_scores_gemma":[0.9986626,0.0006814958,0.0002041157,0.0001491774,0.0002529213,0.0000497122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008087832,0.0002157039,0.006551882,0.0001130603,0.00008378865,0.0001076683,0.00007499372,0.7043008,0.005039365,0.00399899,0.002775472,0.2766574],"study_design_scores_gemma":[0.000001215812,0.00001789648,0.0005819421,0.000007858875,0.00000432796,0.00001131411,0.00001682972,0.9955924,0.0008772273,0.002404788,0.0004799482,0.000004264967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07893766,0.0009830556,0.9141271,0.0008603006,0.0001097339,0.0001067669,0.0002447168,0.002020724,0.002609923],"genre_scores_gemma":[0.8923227,0.0006657103,0.1042242,0.0001736805,0.00008678149,0.0001044232,0.0007798314,0.00005305706,0.00158965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003463861,"threshold_uncertainty_score":0.006887376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318629187139228,"score_gpt":0.2799032618951567,"score_spread":0.2667169700237644,"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."}}