{"id":"W2008414216","doi":"10.1504/ijem.2007.013988","title":"The preventive approach to risks related to interdependent infrastructures","year":2007,"lang":"en","type":"article","venue":"International Journal of Emergency Management","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interdependence; Anticipation (artificial intelligence); Risk analysis (engineering); Probabilistic logic; Risk management; Computer science; Order (exchange); Business; Operations research; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001182853,0.0001380466,0.0001332939,0.0003683918,0.00007865998,0.00004973382,0.0008892952,0.00003256325,0.0001788792],"category_scores_gemma":[0.00007428938,0.0000985681,0.0001882151,0.00031377,0.00001451754,0.0001260313,0.000143166,0.0002231926,0.00003095282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001995491,"about_ca_system_score_gemma":0.000005560466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000942681,"about_ca_topic_score_gemma":0.00002948883,"domain_scores_codex":[0.9981657,0.00004092217,0.0007689376,0.0001278813,0.0006676019,0.0002289355],"domain_scores_gemma":[0.9992707,0.00003496747,0.0001206459,0.0001758148,0.0002710115,0.0001268656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001162536,0.00006617396,0.00184247,0.00002017468,0.002059087,0.00004539349,0.001736727,0.780318,0.000648134,0.006812059,0.01795723,0.1883783],"study_design_scores_gemma":[0.001591262,0.0004403647,0.6603186,0.0002674004,0.0005356639,0.000146415,0.01117029,0.01126898,0.006715262,0.05144855,0.2548168,0.001280442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.242663,0.0002612321,0.6617404,0.0005410169,0.007179481,0.000353074,0.000006188859,0.00004340011,0.08721225],"genre_scores_gemma":[0.9952003,0.0002093154,0.003912214,0.00005960475,0.0002183158,0.000004778092,0.000002264185,0.00001398618,0.000379252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.769049,"threshold_uncertainty_score":0.401949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109832466885838,"score_gpt":0.3083899845053286,"score_spread":0.2974067378167448,"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."}}