{"id":"W2014546172","doi":"10.1115/gt2014-25753","title":"An Efficient Component Map Generation Method for Prediction of Gas Turbine Performance","year":2014,"lang":"en","type":"article","venue":"Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Gas compressor; Operability; Computer science; Turbine; High fidelity; Performance prediction; Gas turbines; Range (aeronautics); Component (thermodynamics); Reliability engineering; Automotive engineering; Engineering; Simulation; Mechanical engineering; Aerospace engineering","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":[0.0003557515,0.0006930464,0.0004069655,0.0005806288,0.0002912653,0.0004208347,0.0006161046,0.0005301815,0.001577654],"category_scores_gemma":[0.00121926,0.0002803906,0.0004028163,0.0005322179,0.0002408373,0.0004197491,0.0003655637,0.0006608624,0.000443653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003167556,"about_ca_system_score_gemma":0.0007156474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005215447,"about_ca_topic_score_gemma":0.004271393,"domain_scores_codex":[0.9998554,0.00003077303,0.000006427179,0.00003238405,0.00006238398,0.00001269949],"domain_scores_gemma":[0.9997222,0.0001219716,0.00002400429,0.00002228007,0.0001006781,0.000008919395],"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":[0.0001111934,0.00004158521,0.0008856569,0.0001144917,0.00003043408,0.00008596948,0.00008816704,0.7116064,0.01626777,0.00348417,0.001971019,0.2653131],"study_design_scores_gemma":[0.00000170922,0.000006513762,0.0001058376,0.00000182597,0.000002109277,0.00000866347,0.00000289962,0.9974359,0.001731382,0.0002403959,0.0004597314,0.000003223704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005947731,0.00006410896,0.9928294,0.00001880335,0.00000959516,0.00002169125,0.00003695496,0.0006577291,0.0004139938],"genre_scores_gemma":[0.3221788,0.0001859006,0.6745129,0.00003300469,0.00002046706,0.0001722697,0.0004047362,0.0002178193,0.002274056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005215447,"threshold_uncertainty_score":0.01037014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007625077735318321,"score_gpt":0.2270729461358133,"score_spread":0.219447868400495,"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."}}