{"id":"W2016158844","doi":"10.1007/s10668-012-9341-0","title":"Can the African food supply model learn from the Asian food supply model? Quantification with statistical methods","year":2012,"lang":"en","type":"article","venue":"Environment Development and Sustainability","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Food supply; Production (economics); Food processing; Agricultural economics; Regression analysis; Linear regression; Business; Agricultural engineering; Environmental economics; Economics; Mathematics; Statistics; Engineering; Food science","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":[0.01275271,0.0007324336,0.001093368,0.0009260358,0.0005341529,0.001709063,0.001384172,0.00117741,0.002675387],"category_scores_gemma":[0.04776262,0.0006428543,0.001016118,0.001213426,0.001925272,0.006019683,0.002458408,0.002808409,0.0001852961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001487465,"about_ca_system_score_gemma":0.002162045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01223931,"about_ca_topic_score_gemma":0.008572641,"domain_scores_codex":[0.9984819,0.0009868424,0.00006943125,0.0002033622,0.0001294921,0.0001290341],"domain_scores_gemma":[0.9672509,0.02812991,0.002051259,0.001427806,0.0008099644,0.0003300825],"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":[0.00004094901,0.00004491244,0.009356595,0.00006498335,0.0001446372,0.00004970776,0.0001413944,0.8452698,0.0001450657,0.1301533,0.0008136359,0.013775],"study_design_scores_gemma":[0.000004387867,0.00001182475,0.0006970434,0.00001568388,0.00001350061,0.000008046122,0.00004102878,0.9061883,0.00006382783,0.09263774,0.0003094962,0.000009049691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1791931,0.0006071117,0.8115775,0.004398531,0.00005436277,0.000048114,0.0003716126,0.000168601,0.003581119],"genre_scores_gemma":[0.9520427,0.0006164554,0.04423105,0.000360186,0.00008514868,0.0001281659,0.0003138606,0.00006197445,0.002160419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01275271,"threshold_uncertainty_score":0.06744361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01992797187913437,"score_gpt":0.2312530300163853,"score_spread":0.2113250581372509,"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."}}