{"id":"W4408924401","doi":"10.3390/jrfm18040177","title":"Finite Mixture at Quantiles and Expectiles","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantile; Econometrics; Environmental science; Computer science; Mathematics","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":[0.0004133916,0.00009851327,0.0001977889,0.0001961574,0.0001538999,0.00008479684,0.0002144841,0.00004842825,0.00000172442],"category_scores_gemma":[0.00005277865,0.0000751979,0.00005876615,0.0001830576,0.00003468904,0.0001583575,0.0002806785,0.0001355784,5.801474e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001608179,"about_ca_system_score_gemma":0.00001522499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004738505,"about_ca_topic_score_gemma":0.000008656634,"domain_scores_codex":[0.9993001,0.00006174314,0.0002346735,0.0001584023,0.0001197161,0.0001254027],"domain_scores_gemma":[0.9995092,0.00009503421,0.0001542319,0.0001452253,0.00004347881,0.00005283117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003018414,0.00002601482,0.00130504,0.00003796838,0.00001698839,0.00006054353,0.0005949837,0.000008884897,0.00001582098,0.2258189,0.002735601,0.769349],"study_design_scores_gemma":[0.001794792,0.0002695393,0.117178,0.0003644414,0.0001726449,0.00007135508,0.0001387397,0.002884218,0.0005081365,0.4624212,0.413842,0.000354873],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02059085,0.004887213,0.9723741,0.0003672481,0.0004316035,0.00006322959,0.000001403001,0.000008840371,0.00127545],"genre_scores_gemma":[0.571308,0.01247617,0.4146766,0.0005417288,0.0001365349,0.000003073705,1.615622e-7,0.00000503286,0.0008526507],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7689942,"threshold_uncertainty_score":0.3066481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006399639355968047,"score_gpt":0.236444589910396,"score_spread":0.2300449505544279,"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."}}