{"id":"W2917950224","doi":"10.5194/acp-19-7347-2019","title":"Modelling CO <sub>2</sub> weather – why horizontal resolution matters","year":2019,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Chemistry; National Oceanic and Atmospheric Administration; European Centre for Medium-Range Weather Forecasts; Environment and Climate Change Canada; Institute for Advanced Studies in Basic Sciences; Japan Aerospace Exploration Agency; Commonwealth Scientific and Industrial Research Organisation; European Commission; California Institute of Technology; National Aeronautics and Space Administration","keywords":"Orography; Environmental science; Greenhouse gas; Atmospheric sciences; Radiosonde; Atmospheric model; Atmosphere (unit); Numerical weather prediction; Climatology; Meteorology; Temporal resolution; Wind speed; Geology; Precipitation; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007694916,0.0003501519,0.0002418636,0.0001239469,0.0001504708,0.001312559,0.0005527291,0.0007965895,0.002897067],"category_scores_gemma":[0.003395773,0.0002356502,0.0003222988,0.0004772844,0.0002905152,0.001519874,0.0002763657,0.0006520278,0.0006511072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005484024,"about_ca_system_score_gemma":0.0004067578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03534715,"about_ca_topic_score_gemma":0.0238749,"domain_scores_codex":[0.9997621,0.00008936537,0.00001073395,0.00006096456,0.00005133569,0.00002542135],"domain_scores_gemma":[0.9989759,0.0005310377,0.0001260174,0.0001196585,0.0002006193,0.00004677526],"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":[0.000330589,0.0001562507,0.106416,0.0004937063,0.0003925216,0.0001509213,0.0001883566,0.7590058,0.01290646,0.009677082,0.01092982,0.09935252],"study_design_scores_gemma":[0.00009051504,0.00006841061,0.05641634,0.0001634198,0.00009899119,0.00006197582,0.0001956774,0.9196125,0.00508531,0.009362997,0.00878534,0.00005843038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8810866,0.007402091,0.06949718,0.01587519,0.000580919,0.00005929398,0.004025442,0.0008398538,0.02063336],"genre_scores_gemma":[0.9871563,0.001130853,0.009600188,0.0002698782,0.00008708906,0.00001131608,0.0006261678,0.0001260111,0.0009923016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03534715,"threshold_uncertainty_score":0.07028282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004049033442651316,"score_gpt":0.170645256204339,"score_spread":0.1665962227616877,"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."}}