{"id":"W4302316314","doi":"10.48550/arxiv.1408.2128","title":"High-dimensional unsupervised classification via parsimonious\\n contaminated mixtures","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada Research Chairs","keywords":"Mixture model; Gaussian; Expectation–maximization algorithm; Dimensionality reduction; Generalization; Covariance; Computer science; Context (archaeology); Mathematics; Mixture distribution; Algorithm; Pattern recognition (psychology); Artificial intelligence; Statistics; Probability density function; Maximum likelihood","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003817897,0.000900369,0.001193947,0.002116046,0.0009331653,0.002612123,0.002565437,0.002256763,0.001400421],"category_scores_gemma":[0.01116416,0.0008934865,0.001869532,0.001870634,0.002067756,0.003026504,0.003002274,0.002162916,0.0008541624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001322839,"about_ca_system_score_gemma":0.001507934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004784173,"about_ca_topic_score_gemma":0.005274077,"domain_scores_codex":[0.9974571,0.001111661,0.0001222553,0.0006157371,0.0004816981,0.0002116023],"domain_scores_gemma":[0.9946879,0.002881869,0.0006086014,0.001011152,0.000590575,0.0002197298],"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":[0.0004250804,0.0001926651,0.007095134,0.0001468938,0.000330366,0.0002449643,0.0006553374,0.5214176,0.006615789,0.1155655,0.005692891,0.3416178],"study_design_scores_gemma":[0.000006551357,0.0000122881,0.0004443651,0.00001206285,0.00001173112,0.00004157664,0.0000285984,0.9604208,0.0007491807,0.03754454,0.000709936,0.00001834943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009471131,0.0001112906,0.9895975,0.0001263452,0.00001355214,0.00002075421,0.00004660434,0.0002529323,0.0003600051],"genre_scores_gemma":[0.4113163,0.0004285499,0.5828977,0.0002891857,0.0001327302,0.0001947361,0.0008805429,0.0002336431,0.003626605],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004784173,"threshold_uncertainty_score":0.02019125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05581815113066747,"score_gpt":0.1973275511852578,"score_spread":0.1415094000545903,"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."}}