{"id":"W3121717060","doi":"10.1017/asb.2018.6","title":"SPATIAL DEPENDENCE AND AGGREGATION IN WEATHER RISK HEDGING: A LÉVY SUBORDINATED HIERARCHICAL ARCHIMEDEAN COPULAS (LSHAC) APPROACH","year":2018,"lang":"en","type":"article","venue":"Astin Bulletin","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Waterloo","funders":"Nanyang Technological University","keywords":"Copula (linguistics); Downside risk; Econometrics; Risk management; Basis risk; Original research; Computer science; Economics; Financial economics; Finance","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":[0.0003421262,0.0001889715,0.0001899138,0.00002028728,0.0002973211,0.00006714219,0.0002478533,0.0001263223,0.0003675178],"category_scores_gemma":[0.0002395879,0.00006881358,0.00004599977,0.0003121504,0.0003066862,0.00004564177,0.0001325435,0.0003005764,0.0001081816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002147158,"about_ca_system_score_gemma":0.00000689849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00343047,"about_ca_topic_score_gemma":0.001647568,"domain_scores_codex":[0.9983992,0.0002213415,0.0002440221,0.0004977175,0.0002519667,0.000385775],"domain_scores_gemma":[0.9994136,0.0002181944,0.000108205,0.00005979477,0.00005878893,0.00014141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001394021,0.0001521058,0.4932648,0.000008624123,0.000008641749,0.00001395005,0.0004808034,0.00001306498,0.01479664,0.0002096784,0.0009322834,0.48998],"study_design_scores_gemma":[0.0002597228,0.0002751436,0.9839052,0.00005081359,0.000008469394,0.00003310568,0.0002851383,0.001287869,0.001327377,0.000342115,0.01197513,0.0002499192],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963441,0.0001889996,0.0001160101,0.001205855,0.00006368433,0.0002620287,0.00001128821,0.00006320517,0.001744778],"genre_scores_gemma":[0.9980773,0.0001204151,0.0008874221,0.00008171841,0.0003511461,0.00001929194,0.00002914946,0.000001590913,0.0004319522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4906404,"threshold_uncertainty_score":0.5185867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008442174009933764,"score_gpt":0.1999505825621145,"score_spread":0.1915084085521807,"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."}}