{"id":"W2079023118","doi":"10.1111/j.1744-7976.2001.tb00324.x","title":"What Future for Agricultural Safety Net Programs?","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gross margin; Safety net; Margin (machine learning); Agriculture; Actuarial science; Crop insurance; Business; Farm programs; Net farm income; Agricultural economics; Farm income; Economics; Finance; Computer science; Geography; Profitability index","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.00274185,0.0003681384,0.0003113508,0.0006816179,0.001587417,0.004683465,0.0008543891,0.004903723,0.02327297],"category_scores_gemma":[0.005733714,0.0001249692,0.0004894368,0.000580327,0.002199374,0.006304571,0.001220206,0.002645895,0.001780039],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004952378,"about_ca_system_score_gemma":0.01323655,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01710605,"about_ca_topic_score_gemma":0.03349454,"domain_scores_codex":[0.9989888,0.0003488221,0.0000219931,0.00008821089,0.0002550574,0.0002970516],"domain_scores_gemma":[0.9961452,0.0009342156,0.0004436984,0.0001173882,0.0008974493,0.001462038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002997757,0.0003318533,0.008979444,0.0008236144,0.00006886643,0.00042803,0.0006948322,0.004057465,0.0008167909,0.5564846,0.1668499,0.2601649],"study_design_scores_gemma":[0.00006428773,0.0003898324,0.01236212,0.002271945,0.00007271262,0.0004360299,0.009604027,0.002603759,0.0005265045,0.2879516,0.6836194,0.0000977392],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02229827,0.03033785,0.002887244,0.8819837,0.001919882,0.00002774919,0.0004863045,0.0001058263,0.05995312],"genre_scores_gemma":[0.7577885,0.08270067,0.01064308,0.09906366,0.004390342,0.0001821144,0.0005831017,0.00008520775,0.04456326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9950476,"threshold_uncertainty_score":0.07785583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187125892741531,"score_gpt":0.1686873368838251,"score_spread":0.149974747609672,"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."}}