{"id":"W2147975952","doi":"10.1002/qj.1895","title":"Comparing TIGGE multimodel forecasts with reforecast‐calibrated ECMWF ensemble forecasts","year":2012,"lang":"en","type":"article","venue":"Quarterly Journal of the Royal Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Ensemble forecasting; Probabilistic logic; Meteorology; Ensemble average; Benchmark (surveying); Range (aeronautics); Computer science; Climatology; Extratropical cyclone; Forecast skill; Artificial intelligence; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001310969,0.0003109914,0.0005872033,0.0000364871,0.0004507469,0.00009245195,0.0006441943,0.0002291937,0.001051251],"category_scores_gemma":[0.00005740067,0.000142145,0.000541597,0.0003325791,0.0002748622,0.0004495954,0.00002909508,0.0007193524,0.00004281145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000026529,"about_ca_system_score_gemma":0.00004163624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001095729,"about_ca_topic_score_gemma":0.00008837382,"domain_scores_codex":[0.9972001,0.0004586802,0.0006945336,0.0002329072,0.0006125568,0.0008012354],"domain_scores_gemma":[0.998062,0.0004650867,0.0005712256,0.0002762551,0.0001477755,0.0004776827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006992218,0.0003366489,0.7734585,0.0000228134,0.0004041433,0.00001427084,0.001861328,0.1124357,0.0001456982,0.0002892368,0.00136117,0.1089713],"study_design_scores_gemma":[0.001520238,0.002961311,0.4814696,0.00003275729,0.0001618066,0.0001012203,0.0004305471,0.5086715,0.00006678566,0.003206623,0.00101902,0.0003586809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878731,0.0006000623,0.007601068,0.000297457,0.0004543558,0.0002315254,0.00001421472,0.00003567138,0.002892527],"genre_scores_gemma":[0.9864578,0.000005010663,0.01244792,0.0005139031,0.0003912679,0.000001648638,0.000008325029,0.000008858068,0.000165217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3962358,"threshold_uncertainty_score":0.9998619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03935324306062764,"score_gpt":0.2257369390553441,"score_spread":0.1863836959947165,"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."}}