{"id":"W4392967945","doi":"10.1016/j.tsep.2024.102541","title":"Prediction of PAN oxidation in a gas turbine bearing chamber using coupled chemical kinetics and CFD simulation of lubricant flow","year":2024,"lang":"en","type":"article","venue":"Thermal Science and Engineering Progress","topic":"Thermal and Kinetic Analysis","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Lubricant; Computational fluid dynamics; Bearing (navigation); Gas turbines; Kinetics; Flow (mathematics); Turbine; Materials science; Mechanics; Environmental science; Chemistry; Mechanical engineering; Computer science; Engineering; Composite material; Physics; Classical mechanics","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":[0.0003956893,0.00007089064,0.0001244753,0.0001196253,0.0000202118,0.00003654105,0.000055232,0.0000293701,0.0000129071],"category_scores_gemma":[0.00003115142,0.00005783694,0.00001239096,0.0004137359,0.0002031775,0.0001856082,0.00005500878,0.00004229801,2.686959e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002570416,"about_ca_system_score_gemma":0.00001782346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005347134,"about_ca_topic_score_gemma":8.128375e-7,"domain_scores_codex":[0.9992695,0.000006872581,0.0001862273,0.0001656692,0.0002268141,0.0001449471],"domain_scores_gemma":[0.999782,0.00002567869,0.00003673886,0.00006656392,0.00005156784,0.00003748461],"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":[0.000008901544,0.00001226195,0.0006759329,0.00008303409,0.000002535908,0.000001125898,0.0004178739,0.121685,0.8720799,0.00002184706,3.460247e-8,0.00501161],"study_design_scores_gemma":[0.00007372323,0.00002776537,0.02097938,0.0001014711,0.00001524693,0.000003758425,0.00001768934,0.7911353,0.1875927,0.000005762553,0.000002384049,0.00004484485],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975035,0.0003599047,0.001948833,0.00001924964,0.0000556563,0.0000764471,0.000003549815,0.00002637332,0.00000645914],"genre_scores_gemma":[0.9983522,0.00001122559,0.001594027,0.000001110477,0.00003024021,0.000002774737,0.000001286522,0.000005558245,0.0000016152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6844872,"threshold_uncertainty_score":0.2358522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608540928193786,"score_gpt":0.2386358930838084,"score_spread":0.2225504838018706,"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."}}