{"id":"W2063410746","doi":"10.1007/bf02530500","title":"Strong consistency of automatic kernel regression estimates","year":2003,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Mathematics; Kernel regression; Independent and identically distributed random variables; Kernel (algebra); Bandwidth (computing); Bounded function; Consistency (knowledge bases); Statistics; Variable kernel density estimation; Applied mathematics; Strong consistency; Regression; Weak consistency; Kernel method; Random variable; Discrete mathematics; Computer science; Mathematical analysis; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0004995406,0.0001417302,0.0004203973,0.00005671091,0.00007193338,0.00001758413,0.0006637439,0.00004768146,0.00002508557],"category_scores_gemma":[0.002483346,0.00008806592,0.00009915402,0.000228339,0.0004054799,0.0001384856,0.0001546337,0.0001231932,0.000004325093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004627704,"about_ca_system_score_gemma":0.0001138356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000255278,"about_ca_topic_score_gemma":0.000001149712,"domain_scores_codex":[0.9985167,0.00007099256,0.0006516029,0.0001506984,0.000432698,0.000177254],"domain_scores_gemma":[0.9980581,0.0005374237,0.0005403102,0.0006108218,0.0001906307,0.00006270404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001318848,0.0002267006,0.0001717806,0.0007214684,0.00003706694,0.000003003738,0.0003377275,0.0004874648,0.0003177062,0.990758,0.0003474286,0.006590314],"study_design_scores_gemma":[0.0003507228,0.0002039802,0.001715582,0.001690587,0.00005655783,0.00003915016,0.00008621871,0.4069844,0.02761238,0.5608168,0.0002188231,0.000224733],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04000298,0.0001492161,0.9535071,0.0004328274,0.0003122707,0.0001727781,0.00003276579,0.00003945018,0.005350671],"genre_scores_gemma":[0.5169595,0.000008884742,0.4829479,0.00002058347,0.000004233623,0.0000018049,8.256185e-7,0.000005210593,0.00005107994],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4769565,"threshold_uncertainty_score":0.3591224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04805841346465264,"score_gpt":0.3353193986665352,"score_spread":0.2872609852018826,"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."}}