{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001617097,0.0001354143,0.0004795898,0.0008831416,0.0000559042,0.00005190397,0.0005294814,0.0001370695,0.00007034421],"category_scores_gemma":[0.001524605,0.00009759548,0.0002571919,0.001689865,0.00008185473,0.0002119272,0.00001537474,0.0004148252,3.205709e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001245012,"about_ca_system_score_gemma":0.0004803514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006459113,"about_ca_topic_score_gemma":0.0005445378,"domain_scores_codex":[0.998201,0.00002989548,0.0008565735,0.0001422338,0.0005256871,0.0002446265],"domain_scores_gemma":[0.9980893,0.0003174761,0.0003587279,0.0002260156,0.0007278129,0.0002806915],"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":[0.00001037986,0.000004652236,0.00003257462,0.000005848187,0.0001047372,5.914869e-7,0.0001785259,0.6697405,0.3288529,0.0009163351,0.00003626616,0.0001167384],"study_design_scores_gemma":[0.0001120905,0.00001731899,0.00006562679,0.00001991848,0.0002757703,0.00002940287,0.00001471483,0.9495813,0.04715695,0.002618555,0.00001564389,0.00009273776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6234161,0.00002498555,0.3762572,0.00004650676,0.0001710399,0.00004148878,0.000008832491,0.000006231599,0.00002754805],"genre_scores_gemma":[0.980204,3.768484e-7,0.01968145,0.000009456126,0.00007869127,7.806289e-7,8.413691e-7,0.00001443387,0.000009916985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3567879,"threshold_uncertainty_score":0.3979828,"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."}}