{"id":"W3125749860","doi":"10.20944/preprints201910.0241.v1","title":"Coupled Stratospheric Chemistry-Meteorology Data Assimilation. Part II: Weak and Strong Coupling","year":2019,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Environment and Climate Change Canada","funders":"Science and Technology Directorate; Environment and Climate Change Canada; York University; European Space Agency","keywords":"Data assimilation; Stratosphere; Troposphere; Atmospheric sounding; Meteorology; Depth sounding; Environmental science; Radiance; Numerical weather prediction; Atmospheric sciences; Chemical transport model; Advection; Atmospheric chemistry; Ozone; Physics; Remote sensing; Geography","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008842412,0.0004847965,0.0006683785,0.00001105579,0.0002896341,0.00007884225,0.001243301,0.0005277019,0.02319249],"category_scores_gemma":[0.0001504085,0.0004765847,0.00008786064,0.0001354979,0.0001962307,0.0002859077,0.001689452,0.0009885973,0.0009560498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001635853,"about_ca_system_score_gemma":0.0003153879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001049082,"about_ca_topic_score_gemma":0.0003222898,"domain_scores_codex":[0.9966201,0.00005838092,0.0006543996,0.001644891,0.0004092393,0.0006130012],"domain_scores_gemma":[0.9967991,0.0001756956,0.0005040971,0.00219956,0.00008172801,0.0002398127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005141029,0.00003058518,0.9358571,0.0001848864,0.0001461884,0.000008342025,0.00009184005,0.06243722,0.0002121622,0.00001131222,0.0001295904,0.000839329],"study_design_scores_gemma":[0.0003956798,0.00003153447,0.5926939,0.00008172212,0.000144137,0.00001407765,0.0003082303,0.397571,0.0001830193,0.000189297,0.007823303,0.0005641554],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9736907,0.001251276,0.0002735083,0.0002255835,0.0007761429,0.0005012531,0.0004011757,0.0001555633,0.02272479],"genre_scores_gemma":[0.9932388,0.001149165,0.0006873921,0.00008370684,0.00031958,0.00000842972,0.002534935,0.00002124906,0.001956758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3431633,"threshold_uncertainty_score":0.9998218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1010081494670935,"score_gpt":0.3025322309421121,"score_spread":0.2015240814750185,"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."}}