{"id":"W3081576430","doi":"10.1016/j.jaerosci.2020.105651","title":"Effective density and metals content of particle emissions generated by a diesel engine operating under different marine fuels","year":2020,"lang":"en","type":"article","venue":"Journal of Aerosol Science","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz; Universität Rostock; Universidad de Buenos Aires; Umweltbundesamt; Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit; Transport Canada","keywords":"Diesel fuel; Soot; Diesel engine; Particle number; Particulates; Particle (ecology); Diesel exhaust; Particle size; Differential mobility analyzer; Mass concentration (chemistry); Gas analyzer; Particle density; Spectrum analyzer; Analytical Chemistry (journal); Materials science; Environmental science; Chemistry; Environmental chemistry; Combustion; Automotive engineering; Physics; Volume (thermodynamics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000539884,0.0001213094,0.000275763,0.00001862636,0.0002362116,0.00004139193,0.0002524652,0.00002772723,0.0005649441],"category_scores_gemma":[0.0002137421,0.00007808008,0.00005702309,0.0004211947,0.0004843809,0.000302352,0.0002061975,0.0001564464,0.000002720958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005461017,"about_ca_system_score_gemma":0.00002964384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006545638,"about_ca_topic_score_gemma":0.000006722125,"domain_scores_codex":[0.998573,0.00004659997,0.0004028243,0.0002208614,0.0005117623,0.0002449497],"domain_scores_gemma":[0.9991149,0.00006978263,0.0001948835,0.00009926179,0.00006668082,0.000454493],"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":[0.00002069663,0.000107588,0.02824241,0.000007426923,0.000008198383,0.000005608085,0.0003387358,0.001216515,0.9683273,0.00001416922,0.00007891493,0.001632481],"study_design_scores_gemma":[0.0005420739,0.0004281305,0.08213316,0.00003151999,0.00003436146,0.00002451642,0.0002147615,0.01791512,0.8985291,0.00001179835,0.00001240541,0.0001230889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941598,0.00009222703,0.004432042,0.0009538691,0.00004251461,0.000139792,0.000003556459,0.000006470365,0.0001697584],"genre_scores_gemma":[0.9984168,0.00004691309,0.001235837,0.0001906943,0.00001584042,0.00000176105,4.482448e-7,0.0000047279,0.00008702624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06979819,"threshold_uncertainty_score":0.6185742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02052997935334254,"score_gpt":0.2311268063042636,"score_spread":0.2105968269509211,"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."}}