{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002824999,0.0005601691,0.0003347436,0.0009144817,0.0002897307,0.000424982,0.0002832702,0.0003565996,0.0002566683],"category_scores_gemma":[0.0008111947,0.0001208477,0.000346103,0.0009133784,0.000158607,0.0003455931,0.0002790898,0.0002838458,0.00009833364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000254338,"about_ca_system_score_gemma":0.0002699081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084247,"about_ca_topic_score_gemma":0.01208016,"domain_scores_codex":[0.9997135,0.00005346815,0.00001638287,0.0001076105,0.00006067779,0.00004848809],"domain_scores_gemma":[0.9996572,0.0001210568,0.00006069509,0.0000508528,0.00007849301,0.00003174116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004868539,0.0009327461,0.6627342,0.0001505908,0.0002964316,0.0004243471,0.0003045067,0.1586492,0.06510891,0.0002782532,0.0008735681,0.1097606],"study_design_scores_gemma":[0.00001119521,0.0002110525,0.6279145,0.000007087352,0.00004601839,0.00008656496,0.000152231,0.3623784,0.008515905,0.0001587528,0.0004896145,0.0000286479],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869993,0.00005748523,0.01168069,0.00002284944,0.00000851882,0.00002397745,0.0007794898,0.0001133138,0.0003143678],"genre_scores_gemma":[0.9938027,0.00002582342,0.004801569,0.000008187665,0.00001012327,0.00001269512,0.001256888,0.000006247087,0.00007583125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01084247,"threshold_uncertainty_score":0.0215587,"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."}}