{"id":"W3137781474","doi":"10.1039/d0ea00020e","title":"Viscosity and liquid–liquid phase separation in healthy and stressed plant SOA","year":2021,"lang":"en","type":"article","venue":"Environmental Science Atmospheres","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Division of Atmospheric and Geospace Sciences; Natural Sciences and Engineering Research Council of Canada; University of California, Irvine; U.S. Department of Energy; Office of Science; University of California; National Science Foundation","keywords":"Viscosity; Scots pine; Aerosol; Phase (matter); Liquid phase; Chemistry; Chromatography; Chemical engineering; Materials science; Organic chemistry; Thermodynamics; Botany; Composite material; Pinus <genus>; Biology; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002019655,0.0001309249,0.000136776,0.000001845585,0.0003140371,0.00008191008,0.0001180985,0.00004785905,0.001724983],"category_scores_gemma":[0.00001909956,0.0001184456,0.00001542347,0.0001794056,0.0005858123,0.0004685576,0.00005294384,0.0001096852,0.00001786362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001861908,"about_ca_system_score_gemma":0.00006840938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003474154,"about_ca_topic_score_gemma":0.000801118,"domain_scores_codex":[0.9987269,0.00003557649,0.0001747544,0.0004677043,0.00027348,0.0003215633],"domain_scores_gemma":[0.9995386,0.00005251431,0.00005463521,0.0001361338,0.000002702887,0.0002154402],"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":[0.001784236,0.0004612172,0.4339067,0.00005536399,0.00001377857,0.0002851273,0.001641378,0.002677605,0.5313528,0.00004547526,0.000141812,0.0276345],"study_design_scores_gemma":[0.003118634,0.002289825,0.64337,0.00007236539,0.00001752477,0.0001954423,0.004327881,0.01476471,0.3286291,0.0001056654,0.002366249,0.0007426378],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975393,0.001285981,0.00002881698,0.0001800119,0.00005831789,0.00009834096,0.00005852203,0.0000129624,0.0007377259],"genre_scores_gemma":[0.9983273,0.0004081861,0.0008463424,0.0002351401,0.00003236589,0.000001357859,0.00005526743,0.000002137947,0.00009191679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2094633,"threshold_uncertainty_score":0.9991876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009203988591224807,"score_gpt":0.2409860812956553,"score_spread":0.2317820927044305,"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."}}