{"id":"W2996369335","doi":"10.1080/07350015.2019.1702047","title":"Dynamic Two Stage Modeling for Category-Level and Brand-Level Purchases Using Potential Outcome Approach With Bayes Inference","year":2019,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Inference; Markov chain Monte Carlo; Outcome (game theory); Econometrics; Computer science; Bayesian inference; Bayesian probability; Bayes' theorem; Monte Carlo method; Markov chain; Statistics; Machine learning; Mathematics; Artificial intelligence","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":[0.000380433,0.0002051753,0.0004410996,0.0002405341,0.0001618789,0.0004085076,0.0001153606,0.0000445946,0.0000611952],"category_scores_gemma":[0.00004750665,0.0001705839,0.00003668695,0.00007335925,0.00005055335,0.0008635676,0.00008662308,0.0001246256,0.000001523108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003309441,"about_ca_system_score_gemma":0.00007967633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000902199,"about_ca_topic_score_gemma":0.0001419393,"domain_scores_codex":[0.9989319,0.000007127142,0.0005268413,0.0002078753,0.0001006637,0.0002255865],"domain_scores_gemma":[0.9989729,0.0001033148,0.0005218636,0.0001051807,0.0002689952,0.00002777059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001368135,0.0001614325,0.6798328,0.002363881,0.0003050065,0.00004434052,0.0001940227,0.2405656,0.0009458448,0.006910093,0.00004050047,0.06726839],"study_design_scores_gemma":[0.002570112,0.00001996698,0.06843235,0.00006686559,0.0002907074,0.00006394518,0.0002756453,0.9268838,0.000001987529,0.001037138,0.00007326189,0.0002841983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5452157,0.00008087965,0.4542422,0.00002476032,0.0001888002,0.0001200353,0.00008775838,0.000004487649,0.00003539719],"genre_scores_gemma":[0.9624661,0.0000585583,0.03715451,0.00006409939,0.0001395861,0.000002407686,0.00002453317,0.00003103229,0.00005915215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6863182,"threshold_uncertainty_score":0.6956208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06962624141778885,"score_gpt":0.2865672080225566,"score_spread":0.2169409666047678,"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."}}