{"id":"W4306836472","doi":"10.3390/atmos13101715","title":"Multivariable Characterization of Atmospheric Environment with Data Collected in Flight","year":2022,"lang":"en","type":"article","venue":"Atmosphere","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Federal Aviation Administration; Environment and Climate Change Canada","keywords":"Icing; Environmental science; Liquid water content; Meteorology; Aerosol; Haze; Atmospheric research; Fog; Icing conditions; Radar; Aviation; Liquid water path; Remote sensing; Radiosonde; Cloud computing; Aerospace engineering; Computer science; Geography; Engineering","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":[0.00008946854,0.0001082408,0.0001686521,0.000002777079,0.00006146068,0.000007296269,0.0003122202,0.00004134274,0.0002879986],"category_scores_gemma":[0.00001039639,0.0001033983,0.000009571854,0.0004373929,0.00002446043,0.00008788691,0.0002097965,0.0001605821,0.0000059162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001052927,"about_ca_system_score_gemma":0.00001973963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001198636,"about_ca_topic_score_gemma":0.00001824393,"domain_scores_codex":[0.9992993,0.0000229344,0.0001772239,0.0001831999,0.0001495682,0.0001677765],"domain_scores_gemma":[0.9993632,0.00002458351,0.00004561984,0.0005454024,0.000006404039,0.00001481754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009041128,0.0001947452,0.04430392,0.0001127893,0.00009973347,0.00004027726,0.0008037155,0.8973652,0.03670056,0.0001934141,0.001634396,0.01846082],"study_design_scores_gemma":[0.001453496,0.0002803822,0.05671228,0.00007509806,0.00003441817,0.00001750942,0.0007771912,0.8854085,0.02424497,0.00005931253,0.03047003,0.0004668249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889788,0.00006476695,0.009347543,0.00003826861,0.00008332044,0.0001900541,0.00003893731,0.0003293891,0.0009288983],"genre_scores_gemma":[0.9895164,0.00004621353,0.009848908,0.00001142931,0.000007644883,0.00004156553,0.0001338018,0.00003025719,0.0003637249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02883563,"threshold_uncertainty_score":0.421646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008327451872207637,"score_gpt":0.1718238055069725,"score_spread":0.1634963536347649,"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."}}