{"id":"W2520907000","doi":"10.1017/s026646661600030x","title":"KERNEL ESTIMATION WHEN DENSITY MAY NOT EXIST: A CORRIGENDUM","year":2016,"lang":"en","type":"erratum","venue":"Econometric Theory","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Kernel density estimation; Estimator; Kernel (algebra); Variable kernel density estimation; Applied mathematics; Limit (mathematics); Multivariate kernel density estimation; Gaussian function; Density estimation; Kernel smoother; Gaussian process; Gaussian; Kernel method; Statistics; Mathematical analysis; Pure mathematics; Artificial intelligence; Computer science; Radial basis function kernel","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":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002446519,0.0005018906,0.001003009,0.000868411,0.0001464417,0.0001127967,0.0005971755,0.0006567523,0.008152839],"category_scores_gemma":[0.01796553,0.0003977093,0.0002424057,0.0004324265,0.0002084762,0.0001241831,0.0002262213,0.000743453,0.001568574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003408455,"about_ca_system_score_gemma":0.0002261711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001805712,"about_ca_topic_score_gemma":0.000004440501,"domain_scores_codex":[0.997151,0.0004821653,0.0008240257,0.0007176099,0.0002941834,0.0005310707],"domain_scores_gemma":[0.9920479,0.00586013,0.0007255266,0.000973478,0.0001557625,0.0002372247],"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":[0.00002453542,0.00004164132,0.00001128541,0.0001576532,0.00005773797,0.000008190726,0.00008095762,5.21122e-8,6.223149e-7,0.4971326,0.3936414,0.1088433],"study_design_scores_gemma":[0.0002380325,0.0000734773,0.0006335585,0.0001797568,0.000119649,0.00001193181,0.00002234399,0.000273592,0.00002778929,0.8635265,0.1343989,0.000494463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00016908,0.0007478275,0.5385593,0.0002134145,0.02772813,0.0005276144,0.0007530371,0.0002103559,0.4310912],"genre_scores_gemma":[0.004641469,0.0003022785,0.2038227,0.0003954555,0.002286619,0.00009490251,0.0001157826,0.0001644573,0.7881764],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3663939,"threshold_uncertainty_score":0.9998475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1157872513424223,"score_gpt":0.3450144665851849,"score_spread":0.2292272152427625,"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."}}