{"id":"W2032331876","doi":"10.1080/00330124.2011.578538","title":"Local Data for Obesity Prevention: Using National Data Sets","year":2011,"lang":"en","type":"article","venue":"The Professional Geographer","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia; McMaster University","funders":"","keywords":"Obesity; Geography; Environmental health; Computer science; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02769203,0.0008674204,0.00181435,0.01287177,0.00214638,0.003139373,0.003823683,0.001000983,0.009267077],"category_scores_gemma":[0.1248493,0.001094749,0.001486766,0.03428904,0.0006688741,0.002859015,0.005501719,0.001783989,0.003301482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005825403,"about_ca_system_score_gemma":0.01250166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.430065,"about_ca_topic_score_gemma":0.4122597,"domain_scores_codex":[0.9747284,0.01050171,0.003781961,0.003292407,0.006649624,0.001045961],"domain_scores_gemma":[0.9276784,0.01681743,0.007738449,0.01838559,0.02738834,0.001991829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004025028,0.0005905302,0.4445638,0.004491301,0.001569688,0.0004316762,0.004575407,0.01670502,0.000667783,0.01840292,0.2855632,0.2220361],"study_design_scores_gemma":[0.0003906722,0.0002073832,0.5212273,0.004236265,0.0009453822,0.0002880186,0.008048257,0.02167656,0.001689002,0.01242328,0.4285046,0.0003633535],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.06737681,0.002163132,0.09039836,0.00293956,0.0004263097,0.0044371,0.7970415,0.00172209,0.03349504],"genre_scores_gemma":[0.1708519,0.001647303,0.2077734,0.0009142094,0.0001411429,0.01192109,0.6029377,0.0005524064,0.003260648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.430065,"threshold_uncertainty_score":0.8551236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1929238522313151,"score_gpt":0.3987043122075756,"score_spread":0.2057804599762605,"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."}}