{"id":"W4287377841","doi":"10.1175/mwr-d-21-0315.1","title":"Using Stochastically Perturbed Parameterizations to Represent Model Uncertainty. Part I: Implementation and Parameter Sensitivity","year":2022,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Data assimilation; Numerical weather prediction; Ensemble forecasting; Uncertainty quantification; Sensitivity (control systems); Advection; Errors-in-variables models; Diabatic; Computer science; Meteorology; Statistical physics; Mathematics; Statistics; Adiabatic process; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006211184,0.000137322,0.0002713587,0.00004534634,0.000386127,0.00003990711,0.00008347264,0.00001916096,0.002855198],"category_scores_gemma":[0.0001530143,0.0001103485,0.00006633106,0.0002412029,0.0000338094,0.00008920905,0.00005382368,0.0001098808,0.00001535138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001449347,"about_ca_system_score_gemma":0.00003161389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003834139,"about_ca_topic_score_gemma":0.0002217691,"domain_scores_codex":[0.9984156,0.0004097324,0.0003154739,0.0003536867,0.0002603737,0.0002450887],"domain_scores_gemma":[0.9991903,0.0002926234,0.00007657,0.0002416019,0.00003361687,0.0001653386],"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":[0.00001858963,0.00002318098,0.005398263,0.00004988771,0.00001974507,0.000006003901,0.0001807563,0.9690437,0.00005995718,0.0002148314,0.0003134232,0.02467171],"study_design_scores_gemma":[0.0001531857,0.0001634402,0.006067289,0.00005188894,0.00009040697,0.000006608534,0.00009186666,0.9804732,0.000001230646,0.001954425,0.01073085,0.0002156146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8988826,0.01946505,0.06799658,0.005161045,0.0002624851,0.003768055,0.001539035,0.0001310474,0.002794118],"genre_scores_gemma":[0.9860457,0.0003809266,0.007553823,0.005572284,0.00003140382,0.00004383813,0.0002664999,0.000006787649,0.0000987747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08716308,"threshold_uncertainty_score":0.9980564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08848529362965846,"score_gpt":0.3193927882620042,"score_spread":0.2309074946323458,"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."}}