{"id":"W4322627292","doi":"10.5194/egusphere-2023-336","title":"A thermodynamic framework for bulk–surface partitioning in finite-volume mixed organic–inorganic aerosol particles and cloud droplets","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Environment and Climate Change Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; Government of Canada","keywords":"Aerosol; Surface tension; Particle (ecology); Volume fraction; Solubility; Volume (thermodynamics); Relative humidity; Work (physics); Particle size; Range (aeronautics); Materials science; Chemical physics; Thermodynamics; Chemistry; Composite material; Physical chemistry; Physics; 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":[0.0008131454,0.0006936213,0.0004936052,0.0006125966,0.0006214787,0.0008188893,0.001835102,0.0009963808,0.001617617],"category_scores_gemma":[0.001079807,0.0003484896,0.001021267,0.0003197985,0.001183931,0.001195453,0.001037527,0.0008464471,0.0003050713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353311,"about_ca_system_score_gemma":0.001125537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007278899,"about_ca_topic_score_gemma":0.005703603,"domain_scores_codex":[0.9997812,0.00008053011,0.000009567889,0.00002183659,0.00007868834,0.00002823141],"domain_scores_gemma":[0.999727,0.0001312838,0.00003425005,0.0000248733,0.00006043178,0.00002222067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001388501,0.00006182772,0.0004946989,0.00006602737,0.00002035532,0.0001360086,0.00007542059,0.7624941,0.01119805,0.2217407,0.0003938945,0.003305042],"study_design_scores_gemma":[0.000003008213,0.000005321011,0.00004180716,0.000002800059,0.00000156981,0.000005552402,0.000006162154,0.9924105,0.0003420188,0.006704145,0.0004741077,0.000002953928],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05321535,0.000756943,0.9329353,0.0003743867,0.0001044854,0.0001180932,0.0001388416,0.0001049149,0.01225168],"genre_scores_gemma":[0.8374241,0.0009895239,0.1481752,0.0002685293,0.0001991551,0.0005798487,0.0002367409,0.0002609243,0.01186605],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007278899,"threshold_uncertainty_score":0.01447302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02392055765402673,"score_gpt":0.2340876654887318,"score_spread":0.2101671078347051,"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."}}