{"id":"W2095315710","doi":"10.1016/j.envres.2014.07.029","title":"Developing small-area predictions for smoking and obesity prevalence in the United States for use in Environmental Public Health Tracking","year":2014,"lang":"en","type":"article","venue":"Environmental Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Health Canada","funders":"Health Canada; Centers for Disease Control and Prevention","keywords":"Behavioral Risk Factor Surveillance System; Environmental health; Poisson regression; Public health; Statistics; Confounding; Random effects model; Poisson distribution; Lasso (programming language); Obesity; Public health surveillance; Geography; Medicine; Demography; Population; Mathematics; Computer science","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":[0.002420382,0.0005104355,0.000459033,0.001313292,0.0003351996,0.0008904269,0.0009097739,0.0004903306,0.001639953],"category_scores_gemma":[0.009265049,0.0004441504,0.0006837629,0.001071719,0.0001320906,0.000870134,0.0007171746,0.000557954,0.0006136479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005086362,"about_ca_system_score_gemma":0.001241083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05586324,"about_ca_topic_score_gemma":0.05775991,"domain_scores_codex":[0.9995523,0.0001390722,0.00003736621,0.0001839487,0.00005393125,0.00003325347],"domain_scores_gemma":[0.9966065,0.002035621,0.0003310367,0.0003296002,0.0005571581,0.0001401219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000210888,0.0003338182,0.8029108,0.00006952922,0.0003247849,0.00008860989,0.0000964734,0.1383181,0.0006832529,0.0009042721,0.005349181,0.0507103],"study_design_scores_gemma":[0.00005650648,0.00008931696,0.1562299,0.0000323852,0.0001087954,0.00004287854,0.0002067475,0.8377314,0.0009299344,0.003282407,0.001268686,0.00002109013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8764043,0.000185453,0.09716636,0.0004692205,0.00004425857,0.0002863993,0.02180408,0.001254977,0.002384812],"genre_scores_gemma":[0.9526342,0.00009205111,0.03381622,0.00006824953,0.00001692242,0.0001676405,0.0125755,0.00004358998,0.0005856403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05586324,"threshold_uncertainty_score":0.1110761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1776237179366116,"score_gpt":0.3694038718864355,"score_spread":0.1917801539498239,"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."}}