{"id":"W1984288371","doi":"10.1016/j.csda.2009.02.021","title":"A non-parametric iterative smoothing method for benchmarking and temporal distribution","year":2009,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; Smoothing; Parametric statistics; Iterative method; Distribution (mathematics); Mathematics; Computer science; Mathematical optimization; Nonparametric statistics; Applied mathematics; Algorithm; Statistics; Mathematical analysis","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":[0.0041939,0.001015036,0.001192685,0.001560894,0.001046688,0.001615501,0.003131331,0.002225014,0.006931709],"category_scores_gemma":[0.01486707,0.0007087246,0.001389918,0.001867729,0.0006618078,0.001660621,0.001808632,0.002179889,0.003006122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006251399,"about_ca_system_score_gemma":0.002499261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003846148,"about_ca_topic_score_gemma":0.006552055,"domain_scores_codex":[0.9982444,0.0005826837,0.0001380877,0.0002543722,0.0006777064,0.0001027969],"domain_scores_gemma":[0.9946066,0.001908302,0.0002617716,0.001322382,0.001730003,0.0001710081],"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":[0.0003880076,0.0003279701,0.001261222,0.0002852009,0.0002962441,0.000222143,0.0002753598,0.1710939,0.0336146,0.0380509,0.01373679,0.7404477],"study_design_scores_gemma":[0.0000207319,0.00004734884,0.0003909242,0.0000130115,0.00003165174,0.0001649354,0.00001898207,0.9770974,0.0106099,0.006311259,0.005251576,0.00004231672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008896281,0.00001678387,0.9973888,0.00001688927,0.00001489506,0.00001831893,0.00003549126,0.001372551,0.0002466069],"genre_scores_gemma":[0.02044131,0.00003341063,0.976596,0.00002894658,0.00001340427,0.0001302723,0.0002742448,0.0009335006,0.001548859],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006931709,"threshold_uncertainty_score":0.02318895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03686833767857196,"score_gpt":0.3661155301217258,"score_spread":0.3292471924431539,"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."}}