{"id":"W4321491235","doi":"10.5194/egusphere-egu23-2394","title":"Coupling simple dry physics to a dynamically adaptive global atmosphere model","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Grid; Climate model; Coupling (piping); Scale (ratio); Atmosphere (unit); Statistical physics; Simple (philosophy); Computer science; Dynamical systems theory; Physics; Meteorology; Climate change; Mathematics; Engineering; Quantum mechanics; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003815379,0.00039963,0.0005059285,0.0002731877,0.0004572239,0.001402776,0.001537735,0.001025424,0.003022198],"category_scores_gemma":[0.001479176,0.0003937732,0.0006806516,0.0003981579,0.0005633751,0.0013431,0.001624019,0.001440143,0.0006151719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000739783,"about_ca_system_score_gemma":0.0008349303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01246803,"about_ca_topic_score_gemma":0.008548189,"domain_scores_codex":[0.999843,0.00004250267,0.00000874358,0.00005118355,0.00003750333,0.00001705415],"domain_scores_gemma":[0.9996524,0.0001236627,0.00003370891,0.000084454,0.00004397105,0.00006180684],"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":[0.00005096433,0.00006228478,0.001896508,0.00002518143,0.00005862591,0.00004393207,0.00004836022,0.9789423,0.002520158,0.009454631,0.001071099,0.005826088],"study_design_scores_gemma":[0.0000301708,0.00001259139,0.000361659,0.000001251471,0.000007857924,0.000004011395,0.000005092925,0.9944747,0.0002738842,0.003279923,0.001541007,0.000007919886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4851465,0.0003259166,0.4540256,0.001727516,0.0004910255,0.0003626582,0.003255778,0.004944901,0.04972018],"genre_scores_gemma":[0.8972076,0.0002134973,0.09082823,0.000417404,0.0001180822,0.0002563958,0.001856484,0.0005788844,0.008523499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01246803,"threshold_uncertainty_score":0.02479094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04811463109560628,"score_gpt":0.2855751454042348,"score_spread":0.2374605143086286,"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."}}