{"id":"W2513265059","doi":"10.2196/mhealth.5961","title":"The Quality and Accuracy of Mobile Apps to Prevent Driving After Drinking Alcohol","year":2016,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Substance Abuse Treatment and Outcomes","field":"Medicine","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queensland University of Technology","keywords":"Mobile apps; Quality (philosophy); Alcohol consumption; mHealth; Mobile device; Computer science; Internet privacy; Alcohol; Environmental health; Applied psychology; Psychology; Medicine; Psychiatry; World Wide Web; Psychological intervention","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006356822,0.0001349092,0.0003312002,0.00005655129,0.0001691092,0.00001106533,0.00005025349,0.00005119999,0.00002088209],"category_scores_gemma":[0.000111168,0.00006675403,0.00004253065,0.0001030198,0.00007370839,0.00007514721,0.00004249511,0.00008218471,0.00000644385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008189512,"about_ca_system_score_gemma":0.0001438852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000376096,"about_ca_topic_score_gemma":0.0002580434,"domain_scores_codex":[0.9986297,0.00008345267,0.0004403908,0.0002497428,0.0002107888,0.0003859538],"domain_scores_gemma":[0.998482,0.0006257267,0.0001716492,0.0002959808,0.00004881927,0.0003758334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004541414,0.000111931,0.7553398,0.0003050206,0.00002569336,0.000003906578,0.003000712,4.962235e-8,0.0001912119,0.0003358479,0.00026549,0.2399662],"study_design_scores_gemma":[0.00162658,0.0003802658,0.9918156,0.0004281853,0.00004608469,0.000009726478,0.0005880274,0.000002005453,0.0003217183,0.0002255169,0.004466699,0.00008958824],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896769,0.003417383,0.00003063897,0.005585827,0.00008423505,0.001053951,0.000002359881,0.00003070559,0.0001180351],"genre_scores_gemma":[0.9935884,0.004266638,0.0002348233,0.001147432,0.00009685772,0.0002560802,0.000001360492,0.00001383283,0.0003945242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2398766,"threshold_uncertainty_score":0.272215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06544409126131738,"score_gpt":0.4237436964633501,"score_spread":0.3582996052020327,"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."}}