{"id":"W2170491249","doi":"10.1115/detc2013-12352","title":"Accounting for Test Variability Through Sizing Local Domains in Sequential Design Optimization With Concurrent Calibration-Based Model Validation","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Sizing; Domain (mathematical analysis); Calibration; Sequential analysis; Design of experiments; Mathematical optimization; Mathematics; Statistics","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":[0.009284933,0.001518,0.001553172,0.001224669,0.0004999318,0.0008605263,0.001621371,0.0008816696,0.001141437],"category_scores_gemma":[0.02014402,0.0008916635,0.001471983,0.0007830958,0.001441341,0.001518514,0.002009443,0.0021223,0.0002671454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009123517,"about_ca_system_score_gemma":0.002163298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002033799,"about_ca_topic_score_gemma":0.002563725,"domain_scores_codex":[0.9944623,0.002918118,0.000260146,0.0006280994,0.001492942,0.0002384331],"domain_scores_gemma":[0.9790526,0.01493696,0.001477214,0.002689964,0.001657537,0.0001857121],"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":[0.0001849089,0.0001434943,0.001872552,0.0000944695,0.00008622596,0.00006399568,0.0001189568,0.8790316,0.009125122,0.006334228,0.0002274214,0.102717],"study_design_scores_gemma":[0.00002550125,0.0001604337,0.0003715579,0.00001260593,0.00001924707,0.00002850888,0.00001262377,0.9871901,0.006975025,0.004679732,0.0005108357,0.00001382009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008522542,0.00004201668,0.9908774,0.00002606453,0.000004639462,0.00003550605,0.000006521206,0.000214066,0.0002711975],"genre_scores_gemma":[0.3053784,0.00005079467,0.69343,0.00007317823,0.00001611863,0.0003478512,0.00005857397,0.0001337205,0.0005112536],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009284933,"threshold_uncertainty_score":0.04910398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942634739263835,"score_gpt":0.2735618950461124,"score_spread":0.244135547653474,"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."}}