{"id":"W7008738664","doi":"","title":"DEALING WITH RISK IN AGRICULTURE: A CROP LEVEL ANALYSIS AND MANAGEMENT PROPOSAL FOR ITALIAN FARMS","year":2020,"lang":"en","type":"dissertation","venue":"Nova Science Publishers (Nova Science Publishers, Inc.)","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wageningen University and Research; Humboldt-Universität zu Berlin; European Agricultural Fund for Rural Development; Ministero delle Politiche Agricole Alimentari e Forestali; European Commission; Università degli Studi della Tuscia; U.S. Department of Agriculture","keywords":"Risk management; Agriculture; Production (economics); Control (management); Order (exchange); Volatility (finance); General partnership; Farm income","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics","sts","scholarly_communication"],"consensus_categories":["sts","scholarly_communication"],"category_scores_codex":[0.003856648,0.001056305,0.001101133,0.001105658,0.00235511,0.01414292,0.00484346,0.0005032111,0.0001747642],"category_scores_gemma":[0.0009830315,0.0004205522,0.0003206609,0.0308989,0.002864031,0.01494756,0.0006032524,0.001124105,0.00001512701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007618325,"about_ca_system_score_gemma":0.001104534,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006881523,"about_ca_topic_score_gemma":0.04280701,"domain_scores_codex":[0.9889268,0.00008091655,0.001133669,0.00382592,0.00371901,0.002313631],"domain_scores_gemma":[0.9950746,0.0001619955,0.001171211,0.0004160354,0.002031791,0.001144349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001795511,0.001969054,0.2177577,0.001246394,0.001483597,0.0002956193,0.01388644,0.003571695,0.1513609,0.02534683,0.01881023,0.562476],"study_design_scores_gemma":[0.001191877,0.0008709121,0.9502742,0.0003003631,0.0007230813,0.00003021389,0.03123311,0.0009146896,0.003192323,0.001142341,0.007615933,0.002510957],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752334,0.0002564973,0.0001395592,0.005595611,0.00152043,0.003356315,0.000217804,0.0002536227,0.01342673],"genre_scores_gemma":[0.9859704,0.0001062715,0.01036295,0.0003185873,0.0002734166,0.000233474,0.001041546,0.00001205489,0.001681341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7325165,"threshold_uncertainty_score":0.9998496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02363342301330666,"score_gpt":0.2599804643260596,"score_spread":0.236347041312753,"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."}}