{"id":"W3023419169","doi":"10.1016/j.ijdrr.2020.101626","title":"Agriculture insurance for disaster risk reduction: A case study of Malaysia","year":2020,"lang":"en","type":"article","venue":"International Journal of Disaster Risk Reduction","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":95,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Business; Disaster risk reduction; Risk management; Agriculture; Risk pool; Business interruption insurance; Context (archaeology); Government (linguistics); General partnership; Environmental planning; Finance; Insurance policy; Key person insurance; General insurance; Income protection insurance; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0004193659,0.0001817237,0.00043258,0.0002184399,0.00009957352,0.00008246784,0.0003298877,0.00007585022,0.00004465461],"category_scores_gemma":[0.0001445522,0.0001690994,0.0002870424,0.0002426658,0.00004891706,0.0006833773,0.00005539019,0.0002952704,0.00002315841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009900096,"about_ca_system_score_gemma":0.00001575951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002727787,"about_ca_topic_score_gemma":0.00001616659,"domain_scores_codex":[0.9980664,0.00004625824,0.001247026,0.0003087847,0.0001700673,0.0001614321],"domain_scores_gemma":[0.9970373,0.00002213308,0.00227467,0.0001585892,0.0004243168,0.00008297437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.006436756,0.006970293,0.564458,0.0002799595,0.005445021,0.0008233258,0.1947219,0.06039593,0.001212518,0.01039862,0.01809546,0.1307622],"study_design_scores_gemma":[0.04771134,0.0189272,0.3272727,0.0007212264,0.001513183,0.01473063,0.4009614,0.01040563,0.003029122,0.04403304,0.126803,0.003891528],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850924,0.0004494767,0.009712856,0.0006551096,0.003052838,0.0004340219,0.0003058021,0.00001073955,0.000286744],"genre_scores_gemma":[0.9964852,0.0004528086,0.0007836346,0.00003345729,0.002077551,0.0000245153,0.000007983198,0.00002110299,0.0001137512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2371854,"threshold_uncertainty_score":0.6895675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02610265563269499,"score_gpt":0.2467536649513087,"score_spread":0.2206510093186137,"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."}}