{"id":"W4388575134","doi":"10.6000/1929-6029.2023.12.22","title":"Comparative Analysis of Predictive Performance in Nonparametric Functional Regression: A Case Study of Spectrometric Fat Content Prediction","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Covariate; Kernel (algebra); Kernel regression; Computer science; Feature selection; Nonparametric regression; Regression analysis; Artificial intelligence; Kernel smoother; Cross-validation; Kernel method; Functional data analysis; Mean squared error; Mathematics; Machine learning; Statistics; Data mining; Support vector machine; Radial basis function kernel","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":[0.006716874,0.0001260244,0.0007988235,0.00673263,0.00004020313,0.00001375393,0.0004076593,0.00009710051,0.0002184116],"category_scores_gemma":[0.02340485,0.00009731889,0.00008411504,0.006106745,0.0002922784,0.0001363466,0.0001761959,0.001051667,0.00000118927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003602904,"about_ca_system_score_gemma":0.0002655393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001603414,"about_ca_topic_score_gemma":0.0002410693,"domain_scores_codex":[0.992245,0.0008179193,0.001702884,0.0002288727,0.004729889,0.0002754592],"domain_scores_gemma":[0.9802051,0.0160608,0.0005857028,0.0001516155,0.002818375,0.00017844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01117942,0.02273177,0.4944757,0.0007164344,0.0108652,0.04974513,0.03474809,0.06541435,0.0006901517,0.1167884,0.003340163,0.1893052],"study_design_scores_gemma":[0.005417935,0.004156558,0.3042654,0.0007361716,0.0002997167,0.0003988315,0.02417606,0.5983647,0.0001895646,0.0618133,0.00002040284,0.0001614555],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7117209,0.00004124687,0.2873639,0.00004195276,0.0002335734,0.0002230191,0.0002368869,0.000003815045,0.0001346931],"genre_scores_gemma":[0.9701289,0.0003567101,0.02934918,0.000003951424,0.00008081507,0.0000202584,0.00001492532,0.000008200157,0.00003708236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5329503,"threshold_uncertainty_score":0.9848214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4373698802794771,"score_gpt":0.5709719315010723,"score_spread":0.1336020512215952,"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."}}