{"id":"W4417481418","doi":"10.1016/j.jval.2025.09.3405","title":"MSR120 How Chinese Restaurants Can Help With Robust Preference Insights: A Novel Dirichlet Mixture Model to Account for Complex Heterogeneity in Individual Preference Weights","year":2025,"lang":"en","type":"article","venue":"Value in Health","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"","keywords":"Preference; Mixture model; Sample (material); Dirichlet distribution; Key (lock); Latent Dirichlet allocation","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":[0.0007035411,0.0002774931,0.0005836405,0.0000962692,0.0002124277,0.0001138648,0.0004684752,0.0001314427,0.000008599706],"category_scores_gemma":[0.0003664145,0.0001064915,0.0000613374,0.001294478,0.00005838735,0.00009632055,0.00009865668,0.0002586226,7.072248e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001326298,"about_ca_system_score_gemma":0.0001103799,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004228321,"about_ca_topic_score_gemma":0.04384634,"domain_scores_codex":[0.9975685,0.0003813536,0.0004690337,0.0006919244,0.0003501008,0.0005390553],"domain_scores_gemma":[0.9984686,0.0009077372,0.0001406179,0.000150622,0.0001186139,0.0002137967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003786703,0.004522534,0.390197,0.001721734,0.0003229644,0.00003687894,0.006880484,0.3714626,0.05275558,0.04206603,0.002488147,0.1237594],"study_design_scores_gemma":[0.000700382,0.0005713229,0.7874895,0.0003574739,0.00001624483,0.000001798741,0.0002194457,0.2032163,0.0001707823,0.006098391,0.0008032823,0.0003551081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770178,0.0001491825,0.01432307,0.006688273,0.00004967724,0.001006578,0.0006866701,0.00002847592,0.00005032407],"genre_scores_gemma":[0.9533016,0.00005396773,0.04465324,0.001628662,0.00004781006,0.0001278727,0.0001252767,0.000002193053,0.00005936284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3972925,"threshold_uncertainty_score":0.973601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2685979868817978,"score_gpt":0.3470187803756831,"score_spread":0.07842079349388537,"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."}}