{"id":"W37344572","doi":"10.17713/ajs.v38i1.257","title":"On Boundary Correction in Kernel Estimation of ROC Curves","year":2016,"lang":"en","type":"article","venue":"Austrian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kernel smoother; Estimator; Mathematics; Smoothing; Kernel (algebra); Boundary (topology); Receiver operating characteristic; Nonparametric statistics; Kernel density estimation; Variable kernel density estimation; Function (biology); Parametric statistics; Applied mathematics; Statistics; Algorithm; Kernel method; Artificial intelligence; Computer science; Support vector machine; Mathematical analysis; Radial basis function kernel","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0008408679,0.0001007847,0.000327306,0.0001420399,0.00002264494,0.00001144127,0.0001098679,0.00005281348,0.0002674214],"category_scores_gemma":[0.01636324,0.00006694653,0.0000369417,0.0001370103,0.00009188519,0.00008770615,0.00001219846,0.0001711929,0.000007096361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007762857,"about_ca_system_score_gemma":0.0001294831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006076269,"about_ca_topic_score_gemma":0.000004638694,"domain_scores_codex":[0.9985043,0.0001685176,0.0007902003,0.00008169602,0.0003224608,0.0001327556],"domain_scores_gemma":[0.9940698,0.004807436,0.0006792159,0.0001163957,0.0002534589,0.0000736498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003285596,0.0003202345,0.001423756,0.0003086285,0.00005440517,0.00007085092,0.0002084556,0.00008730851,0.0004888906,0.3120105,0.06020185,0.6244965],"study_design_scores_gemma":[0.001325649,0.001079926,0.009881048,0.003177553,0.00007049219,0.00005457166,0.00005688297,0.001922791,0.001481098,0.9806583,0.000152971,0.0001387302],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02095643,0.00004104142,0.9776555,0.0002057538,0.0006062845,0.00008500992,0.0001368044,0.000004621876,0.0003085667],"genre_scores_gemma":[0.5639499,0.0001922699,0.435458,0.00004602891,0.00005907799,0.000001540895,0.000001753675,0.00001527151,0.00027611],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6686478,"threshold_uncertainty_score":0.9919223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.085673752318434,"score_gpt":0.3722387698773787,"score_spread":0.2865650175589447,"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."}}