{"id":"W4392169205","doi":"10.1007/s11749-024-00920-2","title":"The orthogonal skew model: computationally efficient multivariate skew-normal and skew-t distributions with applications to model-based clustering","year":2024,"lang":"en","type":"article","venue":"Test","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skew; Skewness; Multivariate statistics; Skew normal distribution; Cluster analysis; Computation; Computer science; Multivariate normal distribution; Mathematics; Applied mathematics; Algorithm; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01598777,0.001787209,0.003262514,0.003737246,0.001502438,0.003056099,0.005887467,0.003000448,0.00755491],"category_scores_gemma":[0.06853585,0.001303591,0.002295669,0.004235013,0.003109724,0.007694613,0.007185534,0.004934839,0.00296301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001678449,"about_ca_system_score_gemma":0.005128138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004435902,"about_ca_topic_score_gemma":0.005208291,"domain_scores_codex":[0.993522,0.003919535,0.000292335,0.0006348732,0.001342607,0.0002886642],"domain_scores_gemma":[0.9677764,0.02268041,0.001683466,0.003588796,0.003092523,0.001178383],"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":[0.0008404573,0.0002813447,0.003478545,0.0003450018,0.000170358,0.0002187279,0.0003381682,0.344752,0.002514461,0.2740723,0.01090104,0.3620876],"study_design_scores_gemma":[0.00003411416,0.00003188703,0.0001757569,0.00001773136,0.000009899605,0.00007284778,0.00002750344,0.8922971,0.000523379,0.1058542,0.0009290661,0.00002649352],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002169504,0.00009632539,0.9968525,0.00009220458,0.00002004801,0.00004422999,0.00008001054,0.0004322632,0.000213041],"genre_scores_gemma":[0.06228508,0.000320868,0.9339974,0.0001851695,0.0001204418,0.0003872552,0.0006873724,0.0005902971,0.001426036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01598777,"threshold_uncertainty_score":0.08455241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537852717219645,"score_gpt":0.2829466552657238,"score_spread":0.2675681280935274,"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."}}