{"id":"W2010956547","doi":"10.1002/cjce.20406","title":"Selection of simplified models: I. Analysis of model‐selection criteria using mean‐squared error","year":2010,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Honeywell (Canada)","funders":"","keywords":"Overfitting; Mean squared error; Selection (genetic algorithm); Model selection; Computer science; Statistics; Nonlinear system; Information Criteria; Mathematics; Data mining; Artificial intelligence; Artificial neural network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.02475239,0.001476357,0.002794135,0.002849594,0.0005749823,0.001585149,0.001474919,0.0009383641,0.001344372],"category_scores_gemma":[0.1037792,0.000656855,0.002227252,0.001441865,0.0009465317,0.001707342,0.002136622,0.001314554,0.0002145141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232381,"about_ca_system_score_gemma":0.002377186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005606771,"about_ca_topic_score_gemma":0.004053704,"domain_scores_codex":[0.9866511,0.009120156,0.0008420424,0.0006906103,0.002410121,0.0002860747],"domain_scores_gemma":[0.9115943,0.07607386,0.004084763,0.003173538,0.004639465,0.0004340788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003826514,0.00005113451,0.004999298,0.0003469574,0.0004957623,0.0001586578,0.00007163376,0.9523678,0.001420827,0.005139105,0.0005283186,0.03403783],"study_design_scores_gemma":[0.00002431417,0.0001353767,0.001498957,0.00002796519,0.00008175718,0.00004317061,0.00001909291,0.9934921,0.0008266934,0.003540952,0.0002873563,0.00002222775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1455112,0.001065093,0.8506956,0.0004169768,0.00003528024,0.0003802962,0.0003782681,0.0003238323,0.001193421],"genre_scores_gemma":[0.7986132,0.0005132907,0.1981386,0.0001745433,0.00004769218,0.0006616657,0.001006384,0.0001220777,0.0007227118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02475239,"threshold_uncertainty_score":0.1309047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004354902123812,"score_gpt":0.3153066754645507,"score_spread":0.2148711852521695,"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."}}