{"id":"W2805584669","doi":"10.5194/acp-18-17029-2018","title":"Application of a hygroscopicity tandem differential mobility analyzer for characterizing PM emissions in exhaust plumes from an aircraft engine burning conventional and alternative fuels","year":2018,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Transport Canada; U.S. Environmental Protection Agency; Federal Aviation Administration; National Aeronautics and Space Administration","keywords":"Jet fuel; Differential mobility analyzer; Tandem; Jet engine; Particulates; Chemistry; Environmental science; NOx; Aerosol; Materials science; Combustion; Aerospace engineering; Organic chemistry","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":[0.0002451121,0.0003515139,0.0001722681,0.0005095692,0.0002116522,0.0002049541,0.0002737384,0.0002939756,0.0005068598],"category_scores_gemma":[0.0002703319,0.0001154703,0.0001845734,0.0002164371,0.0001265038,0.0002088281,0.0002520185,0.0002169882,0.000175141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002118908,"about_ca_system_score_gemma":0.0001900955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001637146,"about_ca_topic_score_gemma":0.002832471,"domain_scores_codex":[0.9998222,0.00001928828,0.000009830843,0.00006349149,0.0000735984,0.00001177034],"domain_scores_gemma":[0.9998859,0.00003559678,0.00001938164,0.000009630339,0.0000387447,0.00001064959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007420385,0.00002550893,0.004121895,0.00003984376,0.00001732667,0.00003899966,0.00002535879,0.0001808193,0.9902754,0.00003117371,0.00004439746,0.005124997],"study_design_scores_gemma":[0.00001110899,0.0003493912,0.02564633,0.000008209639,0.00003236053,0.0002328803,0.00009280795,0.01069656,0.961772,0.00003794365,0.001098443,0.00002207826],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817948,0.0004690004,0.01571839,0.00003127559,0.00002160204,0.0000875698,0.0007111272,0.0001884218,0.0009776942],"genre_scores_gemma":[0.9775595,0.0002847554,0.02079018,0.00004092248,0.000008278777,0.00007377808,0.0004356934,0.00001629418,0.0007905656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001637146,"threshold_uncertainty_score":0.003255248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008226526972958524,"score_gpt":0.24334498536165,"score_spread":0.2351184583886915,"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."}}