{"id":"W3034054763","doi":"10.1177/0844562120932054","title":"Multivariate Outliers: A Conceptual and Practical Overview for the Nurse and Health Researcher","year":2020,"lang":"en","type":"review","venue":"Canadian Journal of Nursing Research","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; McMaster University; Impact","funders":"","keywords":"Outlier; Multivariate statistics; Mahalanobis distance; Leverage (statistics); Multivariate analysis; Computer science; Data mining; Identification (biology); Statistics; Econometrics; Data science; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02474255,0.0002365728,0.001319785,0.0007977083,0.0007340349,0.0008325688,0.0007854772,0.000224883,0.00004318915],"category_scores_gemma":[0.03366646,0.0001314924,0.0002278575,0.001020203,0.00177046,0.0001880299,0.00003563773,0.002068188,0.00001167898],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009460942,"about_ca_system_score_gemma":0.01472803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005360455,"about_ca_topic_score_gemma":0.0002532671,"domain_scores_codex":[0.9943234,0.001868023,0.001061302,0.0004486607,0.001451044,0.0008475164],"domain_scores_gemma":[0.9799995,0.01586419,0.0004345011,0.0004246504,0.001113608,0.002163514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001613027,0.0000118418,0.000001164317,0.0004327569,0.00005186881,0.0001018685,0.002680881,0.000003928633,3.472809e-8,0.006656583,0.08170307,0.9083399],"study_design_scores_gemma":[0.0002696716,0.000438252,0.00002796097,0.01171242,0.00009267239,0.0007942949,0.003952874,0.0005837542,1.931123e-8,0.005808039,0.9761882,0.0001317945],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.049017e-7,0.9685343,0.006849524,0.02299843,0.0004077829,0.001007219,0.00006901735,0.000003339931,0.0001298019],"genre_scores_gemma":[0.0001624485,0.9947785,0.003965077,0.00008377188,0.000588345,0.00002298717,0.000002169481,0.00004863788,0.0003480654],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9082081,"threshold_uncertainty_score":0.9908575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8334423278852698,"score_gpt":0.6376491203259785,"score_spread":0.1957932075592913,"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."}}