{"id":"W4408427016","doi":"10.5194/egusphere-egu25-10952","title":"Aerosol Number Concentration and Cloud Condensation Nuclei Variability During Warm and Moist Intrusions into the Arctic","year":2025,"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":"Environment and Climate Change Canada","funders":"","keywords":"Cloud condensation nuclei; Aerosol; Arctic; Condensation; Atmospheric sciences; Environmental science; The arctic; Cloud computing; Meteorology; Climatology; Oceanography; Geology; Physics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002785037,0.0001766597,0.0001415884,0.0006537585,0.0002080378,0.0004389393,0.0001310649,0.0001499955,0.0002515743],"category_scores_gemma":[0.0006137526,0.0000864205,0.000189833,0.000579197,0.0001587229,0.0001957486,0.0002780047,0.0001175232,0.00005600817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005151478,"about_ca_system_score_gemma":0.0002366116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05894848,"about_ca_topic_score_gemma":0.05657534,"domain_scores_codex":[0.9998986,0.00001349072,0.000006137856,0.00003322713,0.00002500633,0.00002361461],"domain_scores_gemma":[0.9998004,0.00006080796,0.00005593276,0.00001449011,0.00004432915,0.00002405216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002133987,0.00004365375,0.9664481,0.00003878881,0.0001080693,0.0003748065,0.0005598855,0.01067443,0.008361808,0.0002400801,0.0003196536,0.01261735],"study_design_scores_gemma":[0.000002397416,0.00001881656,0.9886043,0.00000638576,0.00001899875,0.00005763376,0.0001467469,0.009325908,0.001286433,0.00004411691,0.0004828258,0.000005528454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991727,0.00003344374,0.0001176285,0.000005253409,0.000001948072,0.000001012846,0.0003236467,0.000009341025,0.0003349202],"genre_scores_gemma":[0.9990808,0.00003732302,0.0001473938,0.000002028733,0.000003451592,0.00000158876,0.0006234174,0.000002914796,0.0001011066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05894848,"threshold_uncertainty_score":0.1172107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008790228578611338,"score_gpt":0.2219464191964492,"score_spread":0.2131561906178379,"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."}}