{"id":"W4290771006","doi":"10.2196/37039","title":"Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Obesity; Socioeconomic status; Disadvantaged; Geospatial analysis; Poverty; Public health; Environmental health; Geography; Social determinants of health; Health equity; Demography; Gerontology; Medicine; Population; Political science; Sociology; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0008135774,0.0002402378,0.0001935676,0.001980765,0.0005807611,0.0006428932,0.0005137611,0.0002268835,0.001179934],"category_scores_gemma":[0.002293281,0.0001246642,0.0005661735,0.002891273,0.0002473489,0.0002907632,0.0006709865,0.0003273433,0.00008441834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001413671,"about_ca_system_score_gemma":0.001444512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3270008,"about_ca_topic_score_gemma":0.4220539,"domain_scores_codex":[0.9996355,0.0001542887,0.00002891623,0.00007420183,0.00005508604,0.00005200055],"domain_scores_gemma":[0.999126,0.0002804133,0.0002198166,0.00008624641,0.0001740342,0.0001134975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002362022,0.00003260981,0.9937549,0.00001155289,0.0000731102,0.00005804989,0.00008618186,0.001596336,0.0000913787,0.00009808254,0.0004439341,0.003730356],"study_design_scores_gemma":[0.000004035642,0.00002998684,0.9675536,0.00003273024,0.0000693221,0.00009240228,0.00114057,0.03022285,0.00006924717,0.0002478985,0.0005277201,0.000009712897],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955958,0.0001531445,0.001305638,0.0002310306,0.000007146466,0.00002488701,0.002121349,0.00002381514,0.0005372728],"genre_scores_gemma":[0.9973399,0.00006095168,0.001469951,0.00002173115,0.000003753862,0.00002030418,0.0009617152,0.000002469313,0.0001191596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3270008,"threshold_uncertainty_score":0.6501948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02245403699272939,"score_gpt":0.2821354559527698,"score_spread":0.2596814189600404,"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."}}