{"id":"W3136880185","doi":"10.1108/ijhma-12-2020-0149","title":"Insurance losses caused by residential housing flood events","year":2021,"lang":"en","type":"article","venue":"International Journal of Housing Markets and Analysis","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flood insurance; Underwriting; Actuarial science; Proxy (statistics); Flood myth; Business; Mortgage insurance; Insurance policy; Casualty insurance; Geography; Statistics","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":[0.0007389677,0.0002574859,0.000227712,0.0006993382,0.0003296322,0.0007679972,0.0002778897,0.0002281046,0.002420706],"category_scores_gemma":[0.004338741,0.0001330851,0.0004289146,0.0006887652,0.000322933,0.0005021634,0.001057296,0.0005991342,0.0002965545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007773078,"about_ca_system_score_gemma":0.000389535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005872188,"about_ca_topic_score_gemma":0.007890076,"domain_scores_codex":[0.9991834,0.0001474323,0.00008143896,0.00009554597,0.0003548275,0.0001373667],"domain_scores_gemma":[0.9961211,0.0007108399,0.002296479,0.0001505757,0.0005203933,0.0002007018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001479624,0.00008351104,0.9805337,0.00005699734,0.0001203776,0.0002683716,0.0003784508,0.002512717,0.0006817789,0.0001938639,0.0006878317,0.01433452],"study_design_scores_gemma":[0.000001453264,0.00008885987,0.996686,0.00001406866,0.0000215955,0.0001951517,0.0005292755,0.001446493,0.0003287312,0.0001349875,0.0005480306,0.000005267401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971208,0.000176441,0.0002749638,0.0001003066,0.00001020616,0.00001754671,0.0004900614,0.00001186602,0.001797683],"genre_scores_gemma":[0.9989085,0.0001171003,0.0001167646,0.00001768692,0.00001183143,0.000007378118,0.000370203,0.000001936401,0.0004486552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005872188,"threshold_uncertainty_score":0.01167601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0126306959156872,"score_gpt":0.2343204244368011,"score_spread":0.2216897285211139,"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."}}