{"id":"W3047940926","doi":"10.5194/amt-14-2327-2021","title":"Monitoring sudden stratospheric warmings using radio occultation: a new approach demonstrated based on the 2009 event","year":2021,"lang":"en","type":"article","venue":"Atmospheric measurement techniques","topic":"Climate variability and models","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Bundesministerium für Verkehr, Innovation und Technologie; Österreichische Forschungsförderungsgesellschaft; National Natural Science Foundation of China","keywords":"Radio occultation; Longitude; Altitude (triangle); Sudden stratospheric warming; Environmental science; Anomaly (physics); Latitude; Meteorology; Event (particle physics); Occultation; GNSS applications; Standard deviation; Satellite; Atmospheric sciences; Remote sensing; Polar vortex; Climatology; Stratosphere; Geodesy; Geology; Geography; Physics; COSMIC cancer database; Statistics; Astrophysics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001296715,0.0002855489,0.0002346754,0.000002882951,0.000303825,0.0001156944,0.0003263078,0.0001228249,0.001230997],"category_scores_gemma":[0.00017008,0.000225663,0.0001272016,0.0007380304,0.0001010492,0.0001940443,0.00008714445,0.0002448566,0.00003358588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006704602,"about_ca_system_score_gemma":0.0002140142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006701843,"about_ca_topic_score_gemma":0.00001562859,"domain_scores_codex":[0.9972903,0.0002464167,0.0004065234,0.0005871396,0.001070693,0.0003988952],"domain_scores_gemma":[0.9989565,0.00007460596,0.0001556088,0.0005889052,0.00007459946,0.0001497793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001895603,0.002426504,0.08473159,0.0001562082,0.0002075449,0.00007674124,0.00165834,0.1977191,0.6030199,0.0005271556,0.01338669,0.09590067],"study_design_scores_gemma":[0.001061115,0.0003275585,0.01006517,0.000614128,0.0002785072,0.00005256506,0.00161152,0.6453924,0.3289745,0.002059984,0.008035309,0.001527303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4881087,0.0002828368,0.4925053,0.0009642636,0.0001997433,0.001542091,0.00000458078,0.0006582994,0.01573424],"genre_scores_gemma":[0.7886172,0.00003527177,0.2106982,0.0003055329,0.00007284548,0.00008285667,0.000003908397,0.00002856515,0.0001555983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4476733,"threshold_uncertainty_score":0.999682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06818597061257518,"score_gpt":0.2647546809222987,"score_spread":0.1965687103097235,"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."}}