{"id":"W2040149518","doi":"10.1002/cjs.10016","title":"Data‐driven choice of the smoothing parametrization for kernel density estimators","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Identity matrix; Smoothing; Parametrization (atmospheric modeling); Kernel (algebra); Mathematics; Kernel density estimation; Applied mathematics; Matrix (chemical analysis); Kernel smoother; Statistics; Kernel method; Computer science; Pure mathematics; Artificial intelligence; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0005553943,0.00009479946,0.0002663431,0.00009493015,0.0001236064,0.00004138245,0.0004795905,0.00005681262,0.00002615707],"category_scores_gemma":[0.02865727,0.0000708794,0.00003984269,0.0001809067,0.0001038594,0.00008154638,0.00001543573,0.0001795291,4.791158e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007125062,"about_ca_system_score_gemma":0.0007263426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004647605,"about_ca_topic_score_gemma":0.00267946,"domain_scores_codex":[0.9989142,0.00007925224,0.0005173571,0.00009847022,0.0001953716,0.0001953496],"domain_scores_gemma":[0.9959592,0.002432498,0.0005346411,0.0003163478,0.0005034539,0.0002539104],"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":[0.00002042109,0.00004574719,0.01003913,0.0001548391,0.00006974874,0.00002316546,0.0004693122,0.0002247952,0.0001442599,0.9058181,0.02882561,0.05416485],"study_design_scores_gemma":[0.0004658577,0.0002247588,0.07591239,0.000228829,0.0002364435,0.0000323863,0.00007990652,0.03520214,0.0002278012,0.8853921,0.001832513,0.0001648789],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01829789,0.0000330345,0.9790931,0.000259965,0.0003139568,0.0001434651,0.001770143,0.000002572796,0.00008592948],"genre_scores_gemma":[0.3508633,0.000003381137,0.6489409,0.0001087136,0.0000502539,2.949299e-7,0.00000786339,0.000008214815,0.00001709269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3325654,"threshold_uncertainty_score":0.9795247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1748366557541502,"score_gpt":0.3776036990121808,"score_spread":0.2027670432580305,"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."}}