{"id":"W2154615866","doi":"","title":"Locally Weighted Full Covariance Gaussian Density Estimation","year":2004,"lang":"en","type":"preprint","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Mathematics; Covariance; Gaussian; Density estimation; Covariance matrix; Dimension (graph theory); Convergence (economics); Applied mathematics; Combinatorics; Statistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002318424,0.0004237456,0.0006020345,0.0001403526,0.0006612574,0.0002512585,0.002631735,0.0003201889,0.00001277838],"category_scores_gemma":[0.00002069206,0.0004689161,0.0001152161,0.0002311412,0.0003326313,0.000953865,0.004097373,0.0004857585,0.00001446906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003347303,"about_ca_system_score_gemma":0.0003726758,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007966632,"about_ca_topic_score_gemma":0.003338246,"domain_scores_codex":[0.996963,0.000133816,0.0005127517,0.001532054,0.0004846411,0.000373727],"domain_scores_gemma":[0.9954843,0.0001248483,0.0009102403,0.002964595,0.0002226317,0.000293371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001724655,0.004468396,0.007457833,0.003595503,0.004811286,0.009597014,0.001728886,0.06497062,0.004899948,0.2579241,0.237523,0.4012987],"study_design_scores_gemma":[0.009287206,0.0007195295,0.03944233,0.003552647,0.002099963,0.001308769,0.0002538867,0.7250781,0.001749932,0.06999575,0.1416195,0.004892411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05412246,0.001604633,0.9335405,0.003841559,0.001282274,0.001373095,0.0005822174,0.000393848,0.003259449],"genre_scores_gemma":[0.9422235,0.002109653,0.05292766,0.0001190403,0.0001965743,0.000004395822,0.0009608733,0.00002638365,0.001431938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.888101,"threshold_uncertainty_score":0.9997762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123768022811354,"score_gpt":0.226048113548053,"score_spread":0.2136713112669176,"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."}}