{"id":"W2026102181","doi":"10.1016/j.jneb.2013.01.021","title":"Characterizing the Food Retail Environment: Impact of Count, Type, and Geospatial Error in 2 Secondary Data Sources","year":2013,"lang":"en","type":"article","venue":"Journal of Nutrition Education and Behavior","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Cancer Institute; Centers for Disease Control and Prevention; National Institutes of Health","keywords":"Census; Geospatial analysis; Type I and type II errors; Statistics; Offset (computer science); Geography; Business; Computer science; Environmental health; Mathematics; Population; Cartography; Medicine","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":[0.02580365,0.0008893675,0.001108744,0.003399482,0.001033985,0.003333862,0.002362972,0.001552051,0.002048182],"category_scores_gemma":[0.1208558,0.0007462298,0.002469165,0.008063925,0.001149634,0.002316713,0.00320522,0.001013535,0.0007317374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001917581,"about_ca_system_score_gemma":0.003238134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1126472,"about_ca_topic_score_gemma":0.08433815,"domain_scores_codex":[0.9681437,0.01618002,0.004545467,0.004615705,0.005025442,0.001489531],"domain_scores_gemma":[0.7847527,0.1639996,0.01895125,0.01742291,0.01307004,0.001803596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000387575,0.0001230399,0.9931293,0.00005364749,0.0005023039,0.00005114765,0.0003487402,0.001471918,0.00008378473,0.0001273223,0.0006316239,0.003089599],"study_design_scores_gemma":[0.00007780745,0.0001695743,0.9754061,0.00008166362,0.0005237181,0.0001857079,0.001614218,0.0182293,0.001028835,0.0005114466,0.002112186,0.00005957387],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844123,0.0001680821,0.003944728,0.0002514827,0.00004147707,0.0000922643,0.0101518,0.00009435829,0.000843477],"genre_scores_gemma":[0.982747,0.00007275743,0.003445914,0.00008942209,0.00001995045,0.0001318695,0.01304329,0.00005809104,0.0003917649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1126472,"threshold_uncertainty_score":0.2239829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03708287364301105,"score_gpt":0.3052283046865379,"score_spread":0.2681454310435268,"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."}}