{"id":"W2944239879","doi":"10.1038/s41612-019-0069-5","title":"Infrared-absorbing carbonaceous tar can dominate light absorption by marine-engine exhaust","year":2019,"lang":"en","type":"article","venue":"npj Climate and Atmospheric Science","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Universität Rostock; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Bundesinstitut für Sportwissenschaft; Natural Resources Canada; Deutsche Forschungsgemeinschaft; European Research Council; National Science Foundation","keywords":"Soot; Arctic; tar (computing); Carbon fibers; Absorption (acoustics); Carbon black; Environmental science; Combustion; Chemistry; Environmental chemistry; Materials science; Organic chemistry; Geology; Oceanography","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":[0.0001667036,0.0003715086,0.0001524207,0.000568442,0.000253091,0.0004974687,0.000202739,0.0002234698,0.001203864],"category_scores_gemma":[0.0001630514,0.0001004003,0.0001889918,0.0002967378,0.0001534386,0.0002992498,0.0002355774,0.0002341311,0.0003020891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003194216,"about_ca_system_score_gemma":0.0001453211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01271363,"about_ca_topic_score_gemma":0.01422452,"domain_scores_codex":[0.9998565,0.0000120734,0.000005911353,0.00004332168,0.00004448767,0.00003773452],"domain_scores_gemma":[0.9998695,0.00002726055,0.00002564759,0.000007025502,0.00005462783,0.00001600708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000777233,0.0001479432,0.1981988,0.0002236833,0.0001085628,0.0002650361,0.0001252848,0.001560209,0.7802711,0.0006899599,0.0006773622,0.0169548],"study_design_scores_gemma":[0.00001559847,0.0002305592,0.6048048,0.00003460194,0.0001243065,0.0002614594,0.0004671089,0.01766771,0.370786,0.000613116,0.004968888,0.00002584031],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934059,0.0006064026,0.001336097,0.00002608023,0.00001312493,0.00001098237,0.0002638628,0.00004535807,0.004292033],"genre_scores_gemma":[0.9987435,0.0001338917,0.0004094185,0.00001599965,0.000004716885,0.00000281362,0.0002398524,0.000007284332,0.0004425591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01271363,"threshold_uncertainty_score":0.02527928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003834742706867494,"score_gpt":0.1833776852433925,"score_spread":0.179542942536525,"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."}}