{"id":"W2540718312","doi":"10.1002/cjs.11331","title":"Switching nonparametric regression models for multi‐curve data","year":2017,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"","keywords":"Frequentist inference; Covariate; Nonparametric statistics; Curve fitting; Nonparametric regression; Function (biology); Mathematics; Data set; Independent and identically distributed random variables; Computer science; Econometrics; Statistics; Bayesian probability; Bayesian inference","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.01724083,0.0008914198,0.002141311,0.003414049,0.0009609826,0.002540885,0.00446042,0.002929589,0.005158522],"category_scores_gemma":[0.05579678,0.0009654352,0.002500106,0.004146681,0.003068533,0.003547793,0.002879597,0.004281498,0.0009748547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002452924,"about_ca_system_score_gemma":0.001030937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01152091,"about_ca_topic_score_gemma":0.008544242,"domain_scores_codex":[0.9919426,0.004745379,0.0002657326,0.001560717,0.001081575,0.0004039147],"domain_scores_gemma":[0.951843,0.03712151,0.004063191,0.004357509,0.002087673,0.0005271731],"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":[0.0001885635,0.0001421636,0.01281682,0.0001941153,0.0002883462,0.0002717871,0.0007407062,0.5909138,0.001261342,0.318452,0.003108461,0.07162188],"study_design_scores_gemma":[0.00001117952,0.00001922701,0.001835977,0.00001906459,0.00001546929,0.00004383946,0.00004763041,0.8862819,0.0001577088,0.1105785,0.0009620768,0.0000274739],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03172256,0.0002286193,0.9656602,0.0005123495,0.00003054581,0.0000716065,0.0003791374,0.0003606869,0.001034221],"genre_scores_gemma":[0.7519732,0.0004500847,0.2386637,0.0002693065,0.0001436027,0.0005072143,0.001879257,0.0003031518,0.00581052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01724083,"threshold_uncertainty_score":0.09117937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2284585116719278,"score_gpt":0.3690791161144387,"score_spread":0.1406206044425109,"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."}}