{"id":"W2958061626","doi":"10.3390/su11143924","title":"Mapping Obesogenic Food Environments in South Africa and Ghana: Correlations and Contradictions","year":2019,"lang":"en","type":"article","venue":"Sustainability","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"University of the Western Cape; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; International Development Research Centre","keywords":"Poverty; Environmental health; Food consumption; Obesity; Neighbourhood (mathematics); Consumption (sociology); Socioeconomics; Urbanization; Low income; Geography; Economic growth; Agricultural economics; Medicine; Economics; Sociology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002199856,0.0002567062,0.0003233862,0.002877319,0.0006137941,0.001719667,0.0003275296,0.000202406,0.0009749165],"category_scores_gemma":[0.008259892,0.0003520614,0.0002841116,0.006024487,0.001632181,0.001134514,0.001842324,0.0002989809,0.0001040414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009416335,"about_ca_system_score_gemma":0.0008592297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03285234,"about_ca_topic_score_gemma":0.05309929,"domain_scores_codex":[0.9985323,0.00079799,0.0001420736,0.0001698844,0.0001695029,0.0001881681],"domain_scores_gemma":[0.995732,0.002043151,0.001380017,0.0002615156,0.0004201715,0.0001631324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004551144,0.000008586707,0.9781331,0.0001091748,0.00008683282,0.0001988059,0.006679275,0.0001643171,0.0003979621,0.0007102268,0.0001166939,0.01334953],"study_design_scores_gemma":[0.000002517142,0.0000197332,0.9752368,0.0001152218,0.00003842294,0.000143456,0.0224106,0.000285887,0.0001159805,0.0005195086,0.001103959,0.000007860162],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968252,0.0008422346,0.0003132794,0.0003057449,0.000004705308,0.000008068512,0.0002070888,0.000003696949,0.001489851],"genre_scores_gemma":[0.9991263,0.0004135897,0.0002800443,0.00002423945,0.00000336065,0.000009231732,0.00008216102,0.000002435049,0.0000587775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03285234,"threshold_uncertainty_score":0.06532228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020002432004663,"score_gpt":0.2265316980155319,"score_spread":0.2163316736954853,"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."}}