{"id":"W2283431843","doi":"10.4271/2009-01-1469","title":"Numerical Investigation of Advanced Compressor Technologies to Meet Future Diesel Emission Regulations","year":2009,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Illinois Nutrient Research and Education Council; Ford Motor Company; U.S. Department of Energy","keywords":"Gas compressor; Diesel fuel; Environmental science; Engineering; Aerospace engineering; Automotive engineering; Systems 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":[0.000302989,0.0003041374,0.0003982812,0.000512358,0.0004457826,0.000881439,0.0004296985,0.0007513794,0.002514729],"category_scores_gemma":[0.0008396091,0.0001698076,0.0004058083,0.000406002,0.0004127755,0.0004338277,0.0003123361,0.000358829,0.0002310291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000705682,"about_ca_system_score_gemma":0.0006939698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006772955,"about_ca_topic_score_gemma":0.005152894,"domain_scores_codex":[0.9998796,0.00002630424,0.000005809371,0.00001638051,0.00004229034,0.00002956681],"domain_scores_gemma":[0.9997703,0.000111075,0.00002751487,0.00001650426,0.00005541173,0.00001909325],"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":[0.0001139367,0.00006281335,0.001911771,0.00008968239,0.00001054614,0.0001103126,0.00003001567,0.9789863,0.009194917,0.002650938,0.0005006554,0.00633811],"study_design_scores_gemma":[0.00001113425,0.000066378,0.0005537039,0.000005060848,0.000005437695,0.00001384608,0.00002718101,0.9968208,0.001777383,0.0001740935,0.0005402389,0.000004792602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285786,0.0006698819,0.03410305,0.0004498213,0.00008054783,0.000107647,0.0003995677,0.0002017595,0.03540923],"genre_scores_gemma":[0.9917049,0.0001712076,0.005566709,0.00002013983,0.000006234133,0.00003884572,0.00008407905,0.00001432001,0.002393461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006772955,"threshold_uncertainty_score":0.01346707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006918425627791477,"score_gpt":0.2264157403499719,"score_spread":0.2194973147221804,"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."}}