{"id":"W4234953474","doi":"10.5194/acp-2018-507","title":"Application of a Hygroscopicity Tandem Differential MobilityAnalyzer for characterizing PM Emissions in exhaust plumes from anAircraft Engine burning Conventional and Alternative fuels","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Vehicle emissions and performance","field":"Engineering","cited_by":1,"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; Particulates; Environmental science; Soot; NOx; Materials science; Tandem; Combustion; Waste management; Chemistry; Nanotechnology; Engineering; Organic chemistry; Nanoparticle; Composite material","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":[0.0002265251,0.0003203896,0.0001554114,0.0005514126,0.0002098701,0.0002525045,0.0002188607,0.0002740593,0.0005885642],"category_scores_gemma":[0.0002074821,0.0001175215,0.0001757974,0.0001807437,0.0001339481,0.0002049729,0.0002592188,0.0001771882,0.0002139783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001988122,"about_ca_system_score_gemma":0.0001885267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001839609,"about_ca_topic_score_gemma":0.003363334,"domain_scores_codex":[0.9998372,0.00001669493,0.000008234082,0.00005931684,0.00006509594,0.00001346779],"domain_scores_gemma":[0.9999298,0.00001789123,0.00001272682,0.000007768436,0.00002456999,0.000007292525],"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":[0.00007436016,0.00002709397,0.006645053,0.00003169056,0.00001494868,0.00002409367,0.00003138277,0.0001924633,0.9864096,0.00002615216,0.0000321349,0.006491053],"study_design_scores_gemma":[0.000007311572,0.0003004472,0.03894258,0.0000067709,0.00002973026,0.0001381887,0.00009457101,0.007336137,0.9522226,0.00003540755,0.0008701483,0.00001615609],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896418,0.0003083867,0.00859089,0.00002160909,0.00001352927,0.00005766443,0.0004844195,0.0001028449,0.0007787565],"genre_scores_gemma":[0.9830245,0.0002574179,0.01512503,0.00003097947,0.00000775491,0.00004849543,0.0004170801,0.00001697999,0.001071827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001839609,"threshold_uncertainty_score":0.003657877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584741014996429,"score_gpt":0.2628363464853141,"score_spread":0.2469889363353498,"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."}}