{"id":"W4293546865","doi":"10.3390/su141710617","title":"Enhanced Agriculture Insurance with Climate Forecast","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Agriculture; Production (economics); Crop insurance; Business; Government (linguistics); Profit (economics); Natural resource economics; Agricultural productivity; Agricultural economics; Volatility (finance); Climate change; Insurance policy; Economics; Finance; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005365852,0.0006274876,0.0005904144,0.0003257463,0.0004408855,0.001585689,0.001248185,0.002184033,0.00610405],"category_scores_gemma":[0.001850019,0.0003359751,0.000702417,0.0003564972,0.0007635214,0.002035333,0.001017924,0.001064124,0.0005268177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001768813,"about_ca_system_score_gemma":0.0014819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01364372,"about_ca_topic_score_gemma":0.005694543,"domain_scores_codex":[0.9997178,0.00007978755,0.000008356286,0.00007473813,0.00005041019,0.00006897028],"domain_scores_gemma":[0.9993865,0.000266668,0.0001594907,0.00003968814,0.00006626614,0.00008134489],"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":[0.00009604418,0.0001045613,0.002606623,0.00004613052,0.00003423748,0.0002124215,0.0001024227,0.9311222,0.001517174,0.05846052,0.001037317,0.004660208],"study_design_scores_gemma":[0.00007094942,0.0001006786,0.00177201,0.00001425518,0.00004100747,0.00007423928,0.00006283715,0.966253,0.0003028508,0.02824577,0.003031781,0.00003050033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6611063,0.001006399,0.2223365,0.007976198,0.0002707383,0.0001696881,0.001653604,0.0005628713,0.1049178],"genre_scores_gemma":[0.9865036,0.0002547048,0.002971683,0.00009133259,0.00002419929,0.00003998772,0.00008265561,0.00001531494,0.01001648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01364372,"threshold_uncertainty_score":0.02712858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004168615337143134,"score_gpt":0.1934049813369156,"score_spread":0.1892363659997725,"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."}}