{"id":"W2522011940","doi":"10.1088/1748-9326/11/9/095013","title":"What commodities and countries impact inequality in the global food system?","year":2016,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Socio-Environmental Synthesis Center; National Science Foundation","keywords":"Inequality; Gini coefficient; Commodity; Redistribution (election); Economics; Food processing; Distribution (mathematics); Food systems; Production (economics); Food distribution; Agricultural economics; International trade; Economic inequality; Food security; Geography; Agriculture; Biology; Food science; Macroeconomics; Political science; Market economy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000933602,0.0004005227,0.0003867194,0.001686414,0.0006736266,0.002418612,0.000276125,0.0004223714,0.00732153],"category_scores_gemma":[0.003227165,0.0001431912,0.0005676318,0.003608719,0.001228894,0.002714965,0.002050314,0.0006228102,0.0003833128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168458,"about_ca_system_score_gemma":0.0006430865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0107028,"about_ca_topic_score_gemma":0.01482559,"domain_scores_codex":[0.9992985,0.00028086,0.00002305549,0.0000777832,0.00008346042,0.0002362961],"domain_scores_gemma":[0.9988287,0.0004572164,0.0003045733,0.0001067266,0.0001409266,0.0001618081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002494707,0.00008265417,0.8236053,0.0003207513,0.0003843734,0.0005416652,0.002182873,0.006233993,0.001447538,0.07126506,0.004639588,0.0890468],"study_design_scores_gemma":[0.00001934322,0.00007685697,0.9009398,0.0003742177,0.0002494899,0.0002102906,0.007508617,0.004802286,0.001062404,0.05689244,0.02782153,0.00004280452],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9018934,0.005476056,0.003417993,0.009137773,0.0001757969,0.00003369534,0.001562964,0.000026776,0.07827565],"genre_scores_gemma":[0.9962797,0.001684506,0.0004446969,0.0003124738,0.00006481021,0.000006513611,0.000239539,0.00001463325,0.0009531825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0107028,"threshold_uncertainty_score":0.02449298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02296528745747422,"score_gpt":0.3023987351132207,"score_spread":0.2794334476557465,"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."}}