{"id":"W4399584283","doi":"10.32614/cran.package.np","title":"np: Nonparametric Kernel Smoothing Methods for Mixed Data Types","year":2006,"lang":"en","type":"dataset","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Nonparametric statistics; Smoothing; Kernel smoother; Kernel (algebra); Computer science; Mathematics; Statistics; Kernel method; Artificial intelligence; Combinatorics; Radial basis function kernel","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003220278,0.0004281155,0.0009363866,0.0002641889,0.0001073423,0.0001607144,0.001629415,0.0004877597,0.0008640809],"category_scores_gemma":[0.03942542,0.0003341959,0.0001182422,0.0003984138,0.00008232704,0.00008674058,0.0006770001,0.0004755986,0.00008110514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004573661,"about_ca_system_score_gemma":0.0001179545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008012229,"about_ca_topic_score_gemma":0.0001133436,"domain_scores_codex":[0.9972197,0.0004488738,0.0007678298,0.0008153151,0.0002842312,0.0004639989],"domain_scores_gemma":[0.9729589,0.02405417,0.0003700693,0.002329921,0.0001684821,0.0001184239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001002402,0.00009987783,4.630025e-7,0.0005610852,0.0000733812,0.000002310578,0.000001644358,2.228307e-7,0.000002585383,0.02010972,0.9288318,0.05030691],"study_design_scores_gemma":[0.0001829152,0.00005510942,0.000003498973,0.00005124407,0.0003483096,0.000003766425,0.000003899499,0.002306819,0.00003405906,0.255611,0.7410529,0.0003464635],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[1.205595e-7,0.0001111347,0.4925951,0.00002761192,0.0004354157,0.0003032065,0.505851,0.00004107748,0.0006352933],"genre_scores_gemma":[3.678356e-8,0.00002206002,0.5145999,0.0001145409,0.0001663903,0.00003687255,0.4844619,0.00002616936,0.0005721198],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2355013,"threshold_uncertainty_score":0.999911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2455108806798172,"score_gpt":0.5069742453004411,"score_spread":0.2614633646206239,"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."}}