{"id":"W2889153167","doi":"10.1115/gt2018-75124","title":"Off-Design Prediction of Transonic Axial Compressors: Part 1 — Mean-Line Code and Tuning Factors","year":2018,"lang":"en","type":"article","venue":"","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"National Aeronautics and Space Administration","keywords":"Gas compressor; Transonic; Computational fluid dynamics; Rotor (electric); Axial compressor; Computer science; Rule of thumb; Mechanical engineering; Line (geometry); Overall pressure ratio; Simulation; Engineering; Aerodynamics; Aerospace engineering; Mathematics; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007225361,0.00008584875,0.0001137998,0.0000719157,0.00005955006,0.00001998955,0.00006077133,0.00007427095,0.0001539565],"category_scores_gemma":[0.00001743805,0.00007392479,0.00002012718,0.00007806125,0.0001030433,0.0001237726,0.000008598502,0.00007207453,0.000004078834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001140529,"about_ca_system_score_gemma":0.000005468594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004242091,"about_ca_topic_score_gemma":0.00001308798,"domain_scores_codex":[0.9995371,0.00001392683,0.0001728478,0.00009544606,0.00007966041,0.0001010693],"domain_scores_gemma":[0.9997715,0.00004020864,0.00002118972,0.0001087368,0.00003471724,0.00002364111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001103412,0.0001793468,0.005478377,0.0003160755,0.0005672869,0.000004632955,0.008605924,0.08385398,0.8091841,0.01924205,0.03671705,0.03574085],"study_design_scores_gemma":[0.0006492265,0.0003495104,0.002789704,0.00008464314,0.00002732444,0.0000036665,0.0005184056,0.5352818,0.4470308,0.0008347551,0.01222926,0.0002009632],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6437023,0.000116886,0.3528553,0.00007599612,0.0002484257,0.0001348225,0.00005088278,0.001238625,0.001576793],"genre_scores_gemma":[0.9970023,0.00009714141,0.00269538,0.000007176875,0.00005344567,0.000005469762,0.00001931352,0.0000113173,0.0001084711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4514278,"threshold_uncertainty_score":0.3014565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04446990812096165,"score_gpt":0.2349026460108646,"score_spread":0.1904327378899029,"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."}}