{"id":"W2150388527","doi":"10.1109/tnn.2009.2016339","title":"A Hybrid Pareto Mixture for Conditional Asymmetric Fat-Tailed Distributions","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Pareto principle; Computer science; Lomax distribution; Mathematics; Mathematical optimization","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00713658,0.001102281,0.001336211,0.002678213,0.0008679355,0.002106036,0.002996247,0.002219568,0.002627725],"category_scores_gemma":[0.0150277,0.0009413272,0.002111116,0.002085177,0.002304388,0.00391482,0.002647795,0.00219466,0.0009494654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00152631,"about_ca_system_score_gemma":0.001300587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004563772,"about_ca_topic_score_gemma":0.004126172,"domain_scores_codex":[0.9977477,0.0007881786,0.0001024417,0.0004708217,0.0007152241,0.0001756152],"domain_scores_gemma":[0.9953899,0.00271407,0.0003565779,0.0007153955,0.0006815491,0.0001424541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002816952,0.0001422216,0.005540919,0.0001497405,0.0002358239,0.0002755817,0.0003270581,0.541853,0.005490702,0.3349516,0.002366417,0.1083852],"study_design_scores_gemma":[0.00001172713,0.00001668379,0.0004528728,0.00001754162,0.0000234475,0.00007308157,0.00001261968,0.9539383,0.0007794393,0.04368848,0.0009582463,0.00002755818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006488983,0.0001223276,0.9925873,0.00008370211,0.00001304365,0.00002536941,0.00004764938,0.0001572891,0.0004743766],"genre_scores_gemma":[0.4681783,0.0007503619,0.5228818,0.0003776118,0.0001322568,0.0002859409,0.0007369663,0.0002169904,0.006439779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00713658,"threshold_uncertainty_score":0.03774232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551099132814183,"score_gpt":0.2623983043234745,"score_spread":0.2468873129953327,"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."}}