{"id":"W3201563899","doi":"10.4018/ijagr.2021100104","title":"The Effect of Socioeconomic and Environmental Factors on Obesity","year":2021,"lang":"en","type":"article","venue":"International Journal of Applied Geospatial Research","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Obesity; Socioeconomic status; Poverty; Geography; Regression analysis; Statistics; Lag; Demography; Population; Association (psychology); Spatial analysis; Econometrics; Environmental health; Mathematics; Medicine; Psychology; Computer science; Economic growth; Economics; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002108314,0.00005307722,0.0001218085,0.00005610794,0.0002886762,0.00008028933,0.0003816505,0.00004968614,0.0002437834],"category_scores_gemma":[0.0001590711,0.00003566619,0.00007541286,0.00003952624,0.0004881054,0.00007995038,0.00005787474,0.0003110327,0.000005180886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001547575,"about_ca_system_score_gemma":0.0001593968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002116512,"about_ca_topic_score_gemma":0.0004203219,"domain_scores_codex":[0.998386,0.0001754782,0.0002428381,0.0001010549,0.0009421384,0.0001524863],"domain_scores_gemma":[0.9983517,0.001253966,0.0001340926,0.00006127584,0.0001216441,0.00007730533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001168601,0.0001679968,0.9313158,0.000009436591,0.0002270258,0.00003186943,0.004179373,0.00003139526,0.00511645,0.004752866,0.0003118906,0.05268724],"study_design_scores_gemma":[0.001300708,0.0004276917,0.8911592,0.00002939412,0.00001963094,0.000001340865,0.004683528,0.00002675304,0.08578779,0.008174967,0.008274542,0.0001144354],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958255,0.00009675247,0.00001170356,0.0005783041,0.0002193398,0.00008292752,0.00001294257,0.000001666932,0.003170839],"genre_scores_gemma":[0.999403,0.0002095925,0.00001056082,0.00001003236,0.0002514391,0.00000153715,0.000004576827,0.000003543553,0.0001057347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08067133,"threshold_uncertainty_score":0.2669258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02011909929444594,"score_gpt":0.3514879850181807,"score_spread":0.3313688857237348,"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."}}