{"id":"W2074581004","doi":"10.1080/02664760701235279","title":"Describing the Dynamics of Attention to TV Commercials: A Hierarchical Bayes Analysis of the Time to Zap an Ad","year":2007,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bayes' theorem; Computer science; Dynamics (music); Bayesian probability; Statistics; Econometrics; Artificial intelligence; Mathematics; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001774254,0.0001090011,0.0003408839,0.0004474144,0.0001338847,0.00007516774,0.0003797525,0.00003745553,0.0001872149],"category_scores_gemma":[0.0002251212,0.0000710103,0.0001254524,0.001177789,0.00005616792,0.0001031212,0.0001413197,0.0001958979,0.000005594766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000391688,"about_ca_system_score_gemma":0.00002941761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001034019,"about_ca_topic_score_gemma":0.0007925231,"domain_scores_codex":[0.9985796,0.00002404967,0.0006860542,0.00009802018,0.0004396699,0.0001726208],"domain_scores_gemma":[0.9984431,0.00033913,0.0006198043,0.0002217386,0.0003411355,0.00003513796],"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":[0.001467932,0.0005086638,0.07683318,0.0001426746,0.001062532,0.00001835685,0.001599431,0.003615288,0.0231234,0.03559752,0.004401237,0.8516298],"study_design_scores_gemma":[0.0005844134,0.00007716008,0.9491955,0.0001530429,0.00506948,0.000004061538,0.00175842,0.03788815,0.000155365,0.002548181,0.00226364,0.0003025653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7825726,0.000009622668,0.214907,0.0006586674,0.0001326996,0.0002127612,0.00005448943,0.000006073265,0.001446173],"genre_scores_gemma":[0.9917989,0.000002076534,0.007500161,0.0005315358,0.00009897808,0.000001028516,0.00001363308,0.00001323264,0.00004042518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8723623,"threshold_uncertainty_score":0.2895716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03077466838236667,"score_gpt":0.2712812290075166,"score_spread":0.2405065606251499,"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."}}