{"id":"W3202723447","doi":"10.1002/cjs.11655","title":"Bayesian spline smoothing with ambiguous penalties","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Smoothing; Penalty method; Spline (mechanical); Smoothing spline; Mathematical optimization; Nonparametric statistics; Computer science; Bayesian probability; Ambiguity; Function (biology); Constraint (computer-aided design); Prior probability; Mathematics; Econometrics; Artificial intelligence; Spline interpolation; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01013492,0.001187122,0.002304675,0.002398543,0.001259502,0.002361384,0.002733078,0.003415943,0.004315561],"category_scores_gemma":[0.03809159,0.001348895,0.002191129,0.003635843,0.001851684,0.00270455,0.002890491,0.00419477,0.00176236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261197,"about_ca_system_score_gemma":0.002625208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008072439,"about_ca_topic_score_gemma":0.00670833,"domain_scores_codex":[0.9934039,0.00391139,0.0002585725,0.0006708727,0.00147979,0.0002756088],"domain_scores_gemma":[0.9907382,0.006470311,0.0005769064,0.001044702,0.0009852232,0.0001846105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000195549,0.00007879717,0.001641669,0.0002251667,0.0001352694,0.0001423606,0.0002693666,0.4458564,0.002405351,0.3823734,0.005339538,0.1613373],"study_design_scores_gemma":[0.00002526206,0.0000199783,0.0003369133,0.00004566497,0.00002329573,0.00005938916,0.00001722257,0.8740939,0.000656754,0.1201251,0.004556566,0.00004002694],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001563012,0.000181747,0.997301,0.0001098905,0.00002800243,0.00001571107,0.00003404867,0.0001511315,0.0006155277],"genre_scores_gemma":[0.1154444,0.0009564899,0.8776568,0.0001913429,0.0001578579,0.0002480631,0.0004407267,0.0003338087,0.00457059],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01013492,"threshold_uncertainty_score":0.05359924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01417739378302661,"score_gpt":0.2223774137203811,"score_spread":0.2082000199373544,"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."}}