{"id":"W4384786323","doi":"10.1016/b978-0-443-15274-0.50133-5","title":"Hybrid Dynamic Surrogate Modelling for a Once-Through Steam Generator","year":2023,"lang":"en","type":"book-chapter","venue":"Computer-aided chemical engineering/Computer aided chemical engineering","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Réseau de cancérologie Rossy","keywords":"Context (archaeology); Residual; Surrogate model; Kriging; Polynomial regression; Computer science; Regression; Gaussian process; Reduction (mathematics); Linear regression; Artificial neural network; Gaussian; Machine learning; Mathematics; Algorithm; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0002325874,0.002395285,0.002178597,0.0005959726,0.0001052142,0.0002798561,0.001322679,0.001317564,0.00004288019],"category_scores_gemma":[0.00003228706,0.002842778,0.001071696,0.0003148784,0.00008274635,0.0005864709,0.000604723,0.002171744,0.0001458866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009429547,"about_ca_system_score_gemma":0.0000765005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003256029,"about_ca_topic_score_gemma":1.956703e-7,"domain_scores_codex":[0.993567,0.000008102234,0.001960346,0.0017732,0.0007900869,0.00190119],"domain_scores_gemma":[0.9970583,0.0004820063,0.0002515129,0.001266445,0.0002553095,0.000686372],"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":[0.00002771434,0.00003473368,1.324091e-7,0.001835734,0.0005784506,0.00006838627,0.0001023101,0.9672202,0.0217875,0.001750353,0.00289889,0.003695518],"study_design_scores_gemma":[0.001458845,0.00008668618,2.729326e-7,0.001182263,0.000209126,0.000105924,6.231184e-7,0.9425849,0.0372079,0.0004844201,0.01396584,0.002713148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007965279,0.0009382709,0.9784889,0.00003755257,0.004241204,0.001078325,0.0003420587,0.006467461,0.0004409416],"genre_scores_gemma":[0.04164082,0.001289382,0.9260438,0.0003333187,0.01387045,0.0007579687,0.006268539,0.00450251,0.00529323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05244513,"threshold_uncertainty_score":0.999979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197563396911141,"score_gpt":0.1897130073088203,"score_spread":0.1777373733397089,"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."}}