{"id":"W2800622002","doi":"10.1021/acs.est.7b02600","title":"Cities’ Role in Mitigating United States Food System Greenhouse Gas Emissions","year":2018,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; American Chemistry Council; U.S. Environmental Protection Agency","keywords":"Greenhouse gas; Urbanization; Natural resource economics; Carbon footprint; Per capita; Food systems; Environmental science; Agriculture; Business; Agricultural economics; Population; Life-cycle assessment; Government (linguistics); Environmental protection; Environmental engineering; Production (economics); Food security; Economic growth; Economics; Geography; Environmental health","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001045277,0.0004103051,0.0001414426,0.0006755425,0.0008892586,0.003628716,0.0005644336,0.0006798782,0.00654583],"category_scores_gemma":[0.001961911,0.0001719007,0.0004138222,0.001125355,0.0005779997,0.001054497,0.001835583,0.0006656097,0.0007430749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003219174,"about_ca_system_score_gemma":0.005246727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03736278,"about_ca_topic_score_gemma":0.1009318,"domain_scores_codex":[0.9992667,0.0002927006,0.00002200987,0.00005469819,0.0001525726,0.0002113011],"domain_scores_gemma":[0.9989471,0.0001491452,0.0002519833,0.00007817379,0.0003686769,0.0002049278],"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":[0.000690829,0.0008450785,0.2730261,0.0009509119,0.0008118229,0.0009705681,0.001741605,0.05385118,0.005621793,0.2168474,0.1496329,0.2950097],"study_design_scores_gemma":[0.000192099,0.0005338211,0.2826511,0.0005175851,0.0007242591,0.0003830611,0.007601558,0.03536617,0.01326592,0.0228141,0.6358255,0.0001249118],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5703328,0.004687041,0.00800056,0.05228298,0.0007803024,0.0001590417,0.003174467,0.0006480277,0.3599348],"genre_scores_gemma":[0.9811398,0.001508419,0.002619167,0.00129889,0.0000601555,0.00004189386,0.0006648223,0.000052591,0.01261424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03736278,"threshold_uncertainty_score":0.07429063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004196139561691486,"score_gpt":0.2002612629600257,"score_spread":0.1960651233983343,"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."}}