{"id":"W2033036691","doi":"10.1007/s11222-011-9278-4","title":"Estimating curves and derivatives with parametric penalized spline smoothing","year":2011,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Smoothing spline; Smoothing; Nonparametric statistics; Parametric statistics; Spline (mechanical); Thin plate spline; Mathematics; Functional data analysis; Semiparametric model; Mathematical optimization; Function (biology); Parametric model; Nonparametric regression; Applied mathematics; Computer science; Econometrics; Statistics; Spline interpolation; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0004737941,0.0001600595,0.0002993413,0.00005659875,0.0001912781,0.00005635319,0.00006759517,0.00002852173,0.00004418944],"category_scores_gemma":[0.002454758,0.0001221629,0.00000948128,0.0001582991,0.000158503,0.00004030802,0.00009634869,0.0001507744,6.633084e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006116632,"about_ca_system_score_gemma":0.00002000917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005746257,"about_ca_topic_score_gemma":0.000003476917,"domain_scores_codex":[0.9989943,0.0000891946,0.0002983598,0.0002478075,0.0001476084,0.0002227157],"domain_scores_gemma":[0.996941,0.002559342,0.0001843149,0.0001127874,0.0001040803,0.00009851148],"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":[0.00002968063,0.00006462673,0.006585812,0.001119405,0.00005931701,0.00003594498,0.00222738,0.000004684978,0.00003093551,0.8342198,0.0001713628,0.1554511],"study_design_scores_gemma":[0.0005273803,0.0002905692,0.01833156,0.0009988224,0.00009496554,0.00004935783,0.0002748882,0.2810887,0.00007937649,0.6979207,0.0000129787,0.0003307204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08618274,0.0002686073,0.9123016,0.00001765091,0.00004087761,0.000127224,0.00002713942,0.00004136128,0.0009927963],"genre_scores_gemma":[0.27947,0.00002811306,0.720379,0.00007175084,0.00002132026,0.000002102058,0.000002184294,0.00001379624,0.00001168282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.281084,"threshold_uncertainty_score":0.4981659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1083247700227626,"score_gpt":0.3590536582647492,"score_spread":0.2507288882419866,"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."}}