{"id":"W4403134835","doi":"10.1021/acs.est.4c05650","title":"Molecular Insights into Gas–Particle Partitioning and Viscosity of Atmospheric Brown Carbon","year":2024,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation; National Oceanic and Atmospheric Administration; United States-Israel Binational Science Foundation","keywords":"Particle (ecology); Environmental science; Viscosity; Carbon fibers; Atmospheric sciences; Chemistry; Environmental chemistry; Materials science; Thermodynamics; Physics; 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":[0.0001709606,0.0002331296,0.00008960864,0.0003032656,0.0001414657,0.0002568122,0.0001407036,0.0002374527,0.0006390139],"category_scores_gemma":[0.0002283037,0.0001259573,0.0001624378,0.0001138131,0.000222392,0.0005105681,0.0001868857,0.000449724,0.0001015614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00019734,"about_ca_system_score_gemma":0.0001250745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007225169,"about_ca_topic_score_gemma":0.0008635786,"domain_scores_codex":[0.9999353,0.000007363583,0.000002326571,0.00001657029,0.00002337875,0.00001499509],"domain_scores_gemma":[0.9999372,0.00002588797,0.00001440797,0.000005253389,0.0000108039,0.000006446575],"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":[0.00006135879,0.00002765067,0.004363066,0.00005587597,0.00001738454,0.00004317719,0.00003918486,0.001711152,0.9891347,0.0004853839,0.00003739666,0.004023747],"study_design_scores_gemma":[0.00001343313,0.0002014619,0.0646158,0.00001286458,0.00004135093,0.0001543405,0.0001582732,0.0457071,0.8858474,0.001153358,0.002066513,0.00002795338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944959,0.0005611115,0.00412581,0.00003868748,0.000006811048,0.000008195995,0.000151575,0.00002331119,0.0005886444],"genre_scores_gemma":[0.9976783,0.0003329361,0.001619096,0.00001463684,0.000005904543,0.000005012821,0.0001350652,0.000007589873,0.000201519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007225169,"threshold_uncertainty_score":0.002137721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003066841333542181,"score_gpt":0.177621861957091,"score_spread":0.1745550206235488,"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."}}