{"id":"W4283727719","doi":"10.1175/bams-d-21-0234.1","title":"Skill of Medium-Range Forecast Models Using the Same Initial Conditions","year":2022,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Office of Naval Research; Met Office; Department for Environment, Food and Rural Affairs, UK Government","keywords":"Initialization; Forecast skill; Range (aeronautics); Quantitative precipitation forecast; Computer science; Ensemble forecasting; Forecast verification; Quality (philosophy); Global Forecast System; Consensus forecast; Econometrics; Meteorology; Environmental science; Precipitation; Numerical weather prediction; Mathematics; Machine learning; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0008952385,0.0001470095,0.0003920304,0.00001764615,0.0007642852,0.00001067514,0.0007780099,0.00004135455,0.01184045],"category_scores_gemma":[0.0001663522,0.00007574865,0.0004841602,0.0004599861,0.001687152,0.000022991,0.0001932098,0.0003712127,0.000005901737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001161662,"about_ca_system_score_gemma":0.00004189193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001901059,"about_ca_topic_score_gemma":0.00002703359,"domain_scores_codex":[0.9977208,0.0007927626,0.0004041262,0.0002417408,0.0005291958,0.0003113302],"domain_scores_gemma":[0.9977416,0.001298296,0.000488899,0.0003358136,0.00005873309,0.00007668492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002708588,0.0002393876,0.05850193,0.00001524992,0.0002212046,0.000002743858,0.001179664,0.9263062,0.0005948567,0.002026462,0.007837337,0.002804121],"study_design_scores_gemma":[0.001466375,0.003026171,0.6070134,0.00001255884,0.00044843,0.00005262976,0.009984278,0.2764129,0.0002243614,0.07783537,0.02278881,0.000734734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924541,0.0002319637,0.000582448,0.00369088,0.0001463151,0.000308813,0.000574024,0.00002261385,0.001988851],"genre_scores_gemma":[0.9921104,0.00002244218,0.003167249,0.004528257,0.00006413049,0.000009001088,0.00002813216,0.000004081441,0.00006628574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6498933,"threshold_uncertainty_score":0.9890628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04799898762819969,"score_gpt":0.2653129577175222,"score_spread":0.2173139700893225,"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."}}