{"id":"W237903027","doi":"","title":"Preventing Disaster after a Disaster: Lessons for Canada from US Experience","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fraser Institute","funders":"","keywords":"Property insurance; Legislation; Quake (natural phenomenon); Earthquake casualty estimation; Natural disaster; Hurricane katrina; Population; Payment; Geography; Flood myth; Government (linguistics); Forensic engineering; Business; Political science; Insurance policy; Actuarial science; Engineering; Casualty insurance; Urban seismic risk; Law; Seismology; Finance; Sociology; Demography; Archaeology; Civil engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004084299,0.0007475772,0.0006868014,0.001513649,0.01776401,0.00735569,0.003009452,0.003906831,0.01407619],"category_scores_gemma":[0.009030481,0.0004843783,0.0009252036,0.00356847,0.003925234,0.003581306,0.004668414,0.007896197,0.001036475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08772736,"about_ca_system_score_gemma":0.407016,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.992486,"about_ca_topic_score_gemma":0.9978589,"domain_scores_codex":[0.996048,0.0005403648,0.0001577613,0.0001618195,0.0009994569,0.002092588],"domain_scores_gemma":[0.9780465,0.001063946,0.0002317382,0.0002306996,0.009153293,0.0112738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000256042,0.0005581235,0.02227111,0.0008772328,0.00006903235,0.002402173,0.0151637,0.00104939,0.0005343102,0.01620542,0.7702351,0.1703784],"study_design_scores_gemma":[0.0003460313,0.0003554896,0.03502528,0.003091317,0.000108763,0.0008388882,0.1042838,0.0008774093,0.0005347275,0.00865545,0.8456158,0.0002670718],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.04445026,0.01962509,0.001494574,0.8111901,0.004983352,0.000364545,0.002398464,0.0002724172,0.1152212],"genre_scores_gemma":[0.5036001,0.07919563,0.01879732,0.2858942,0.0009973692,0.0004567892,0.00331891,0.000627131,0.1071126],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.08772736,"threshold_uncertainty_score":0.6365095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02199188892066043,"score_gpt":0.2122857382646216,"score_spread":0.1902938493439612,"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."}}