{"id":"W2893913719","doi":"10.36001/phmconf.2018.v10i1.495","title":"Ensemble Learning Based Surrogate Modeling for Gas Turbine Blisk Temperature Predictions","year":2018,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Life Prediction Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Surrogate model; Computer science; Computational fluid dynamics; Computational complexity theory; Ensemble forecasting; Ensemble learning; Artificial intelligence; Machine learning; Algorithm; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001134302,0.0004763748,0.0008906497,0.0003526687,0.0002908247,0.000687881,0.0005852049,0.0007658489,0.0007638086],"category_scores_gemma":[0.002660698,0.0003459878,0.0006813668,0.0004278836,0.0002973824,0.0007204504,0.0006183762,0.001041116,0.0002068735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004071064,"about_ca_system_score_gemma":0.0005351224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002698176,"about_ca_topic_score_gemma":0.002297784,"domain_scores_codex":[0.9995921,0.0001859231,0.00002335447,0.00004923137,0.0001170187,0.00003233334],"domain_scores_gemma":[0.9988732,0.0006303091,0.0001318868,0.0001170876,0.0002097954,0.00003768501],"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":[0.00001615087,0.000007662152,0.0002258841,0.000005600673,0.000006212519,0.000007215464,0.000004295578,0.9949821,0.0004737928,0.0004106667,0.00004674643,0.003813641],"study_design_scores_gemma":[3.179614e-7,0.000003401001,0.00003144598,5.218439e-7,4.658738e-7,8.827698e-7,4.402882e-7,0.999681,0.0001304423,0.0001317705,0.00001856828,6.615298e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08817848,0.0002226368,0.9096595,0.0001183427,0.00003050675,0.00002370932,0.0001020207,0.0003285659,0.001336253],"genre_scores_gemma":[0.9516423,0.0001370371,0.04692583,0.00002989427,0.00001506427,0.00006598461,0.0002009518,0.00003531127,0.0009476104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002698176,"threshold_uncertainty_score":0.00599885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01493897607389456,"score_gpt":0.2224616100307671,"score_spread":0.2075226339568725,"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."}}