{"id":"W2611066518","doi":"10.5210/ojphi.v9i1.7715","title":"Estimating spatial patterning of dietary behaviors using grocery transaction data","year":2017,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transaction data; Metropolitan area; Sample (material); Census; Marketing; Market basket; Business; Geography; Direct marketing; Database transaction; Advertising; Environmental health; Medicine; Computer science; Economics; Population; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002260946,0.0001670387,0.0007201373,0.0002324769,0.0003012539,0.00009053406,0.0005503744,0.00008961975,0.00002252193],"category_scores_gemma":[0.0008592447,0.0001414703,0.000120969,0.00009225314,0.0001042593,0.0026987,0.0001760057,0.0007552804,0.000001368555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001899446,"about_ca_system_score_gemma":0.001121623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000459435,"about_ca_topic_score_gemma":0.00006700062,"domain_scores_codex":[0.9968607,0.00007483693,0.0018465,0.00008417429,0.0007655563,0.0003682646],"domain_scores_gemma":[0.9942804,0.00008429716,0.004006824,0.0007185639,0.0004639315,0.0004459902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007062306,0.008461131,0.33046,0.005018431,0.0002786185,0.0000335897,0.003391987,0.0009348308,0.000372468,0.00001106171,0.0003354347,0.6506318],"study_design_scores_gemma":[0.001667252,0.001039101,0.5426167,0.001147392,0.0001830131,0.0003848943,0.0008371221,0.4514374,0.00006589583,0.0000357151,0.0004288485,0.0001566631],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931275,0.00002786702,0.06491221,0.002911639,0.0005503745,0.0001558525,0.0001149342,0.00001241886,0.00003972482],"genre_scores_gemma":[0.9042659,0.00003350163,0.09458151,0.0002955734,0.0007077514,2.644321e-7,0.00009265926,0.00001845405,0.000004405278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6504752,"threshold_uncertainty_score":0.5768993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2118974569209651,"score_gpt":0.4266714885379528,"score_spread":0.2147740316169877,"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."}}