{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009672498,0.0001702271,0.0002352103,0.00005224739,0.0003191017,0.00001252285,0.001009903,0.0001004502,0.0005755435],"category_scores_gemma":[0.0001008057,0.0001096452,0.00009704816,0.0002366689,0.0002708278,0.0004502579,0.001679851,0.0002956403,0.0000535758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002668145,"about_ca_system_score_gemma":0.0002688716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000064887,"about_ca_topic_score_gemma":0.00005565702,"domain_scores_codex":[0.9981879,0.0001323394,0.0002287606,0.0005216785,0.0006412955,0.0002880553],"domain_scores_gemma":[0.997888,0.0001881465,0.0001245642,0.001449357,0.0002363756,0.0001135499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00214209,0.06194091,0.7524229,0.0009871452,0.00213706,0.00003100917,0.0006041704,0.0000267117,0.00133371,0.02213958,0.1426425,0.01359217],"study_design_scores_gemma":[0.001727853,0.0001932599,0.8955299,0.0002895458,0.0005050045,0.00003737257,0.0002398735,0.02491138,0.0003281963,0.0691703,0.006753264,0.0003140457],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832902,0.0002718884,0.006567319,0.001634264,0.001017756,0.002020074,0.001499099,0.0001349702,0.003564449],"genre_scores_gemma":[0.9891757,0.000005945609,0.007204344,0.000475841,0.0003571026,0.00002857376,0.001372217,0.00002690003,0.001353386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.143107,"threshold_uncertainty_score":0.6301798,"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."}}