{"id":"W2078831675","doi":"10.5547/issn0195-6574-ej-vol29-nosi-2","title":"Perspectives on Nonparametric and Semiparametric Modeling","year":2008,"lang":"en","type":"article","venue":"The Energy Journal","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nonparametric statistics; Exploit; Econometrics; Curse of dimensionality; Semiparametric regression; Economics; Curse; Nonparametric regression; Function (biology); Regression; Regression analysis; Computer science; Mathematics; Statistics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00733216,0.00095941,0.001336863,0.001676663,0.0008787024,0.004445764,0.001845524,0.002736899,0.004752093],"category_scores_gemma":[0.01350109,0.0005094844,0.001620938,0.002129687,0.004787016,0.003955069,0.002605424,0.004733794,0.0008903035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001967836,"about_ca_system_score_gemma":0.001734552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002048857,"about_ca_topic_score_gemma":0.001409512,"domain_scores_codex":[0.9947156,0.003323149,0.0001982133,0.0004978764,0.001022377,0.0002426815],"domain_scores_gemma":[0.9861954,0.01133918,0.0005425068,0.0009715619,0.0006700045,0.0002812945],"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":[0.000002658533,0.000007473039,0.00009296557,0.00002802231,0.000009042343,0.00002128116,0.00004197822,0.003073302,0.00002818964,0.9901403,0.0008237134,0.005731109],"study_design_scores_gemma":[0.000002693236,0.000006168419,0.00009298854,0.00002812541,0.000002959403,0.00002471437,0.00002188173,0.01280224,0.00002364414,0.9784228,0.00856524,0.000006437593],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003886736,0.01526462,0.9241463,0.01809481,0.0006134505,0.00002786997,0.0003858295,0.0001086766,0.03747168],"genre_scores_gemma":[0.5535821,0.04403356,0.3643633,0.006244096,0.00964646,0.0005404225,0.0006332511,0.0002493343,0.02070742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00733216,"threshold_uncertainty_score":0.03877664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09451577439524025,"score_gpt":0.3438971993893625,"score_spread":0.2493814249941222,"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."}}