{"id":"W2339625210","doi":"10.5194/acp-16-11107-2016","title":"Effects of 20–100 nm particles on liquid clouds in the cleansummertime Arctic","year":2016,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Toronto; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Max-Planck-Institut für Chemie; Environment and Climate Change Canada","keywords":"Aerosol; Cloud condensation nuclei; Atmospheric sciences; Environmental science; Liquid water content; Altitude (triangle); Arctic; Meteorology; Climatology; Cloud computing; Geography; Oceanography; Physics; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001286702,0.0001809059,0.0002018182,6.218826e-8,0.00007438451,0.00001747848,0.0002265241,0.00007372283,0.0006991813],"category_scores_gemma":[0.00004624321,0.0001039078,0.00006512697,0.0002025145,0.0002060519,0.00009011072,0.00001058894,0.0001258892,0.00003489662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005508644,"about_ca_system_score_gemma":0.00002501135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363448,"about_ca_topic_score_gemma":0.00000724135,"domain_scores_codex":[0.9990206,0.00004458821,0.0002042512,0.0002630912,0.000195104,0.0002724147],"domain_scores_gemma":[0.9989067,0.0006674398,0.00008580051,0.0002431827,0.000017807,0.00007907069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009744411,0.0004887904,0.09299257,0.0009899839,0.00009796394,0.0000716492,0.001174149,0.001253494,0.2433554,0.0001216024,0.0005122203,0.6579677],"study_design_scores_gemma":[0.002425792,0.000655018,0.1088986,0.0006457505,0.00009774097,0.00003158787,0.000378103,0.003822648,0.8741728,0.003984542,0.004095027,0.0007923585],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964434,0.0003739713,0.0001026528,0.0002533612,0.00003067495,0.0001007825,0.00001100936,0.00001680963,0.002667372],"genre_scores_gemma":[0.9984723,0.0001351463,0.0003965907,0.000187339,0.0001666713,0.000004188307,0.000007633419,0.000004882672,0.0006251943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6571754,"threshold_uncertainty_score":0.7655545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007114997376475064,"score_gpt":0.1962520728678481,"score_spread":0.1891370754913731,"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."}}