{"id":"W2810202692","doi":"10.1002/qj.3370","title":"Introduction to the special issue on “25 years of ensemble forecasting”","year":2018,"lang":"en","type":"article","venue":"Quarterly Journal of the Royal Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Probabilistic logic; Range (aeronautics); Meteorology; Ensemble forecasting; Climatology; Environmental science; Weather prediction; Computer science; Scale (ratio); Probabilistic forecasting; Weather forecasting; Econometrics; Geography; Artificial intelligence; Mathematics; Cartography; Engineering; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004781247,0.001918092,0.001646335,0.003333547,0.001242924,0.004744797,0.001965615,0.002635925,0.04183563],"category_scores_gemma":[0.01804119,0.0006324216,0.001259647,0.004256593,0.001288043,0.005795379,0.002762995,0.006177323,0.02072836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001426218,"about_ca_system_score_gemma":0.002193992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002379907,"about_ca_topic_score_gemma":0.003376402,"domain_scores_codex":[0.9974269,0.0006711536,0.0003078488,0.0004766564,0.0009692201,0.0001482645],"domain_scores_gemma":[0.9895505,0.004519449,0.0007316286,0.0008685322,0.003444648,0.0008851799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002279775,0.00001571482,0.0002507163,0.0002561338,0.00002873681,0.00003177558,0.00004642689,0.0005911263,0.0001137512,0.006604007,0.920164,0.07187478],"study_design_scores_gemma":[0.000003344244,0.00001540837,0.000335593,0.000286926,0.00001059015,0.00006486323,0.00002230563,0.0005303089,0.00007427379,0.005711308,0.9929308,0.00001442257],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0006619577,0.1291244,0.03674089,0.06779605,0.7282008,0.0001310325,0.002267013,0.0009633453,0.03411451],"genre_scores_gemma":[0.006293873,0.1122524,0.0128026,0.02202209,0.7846186,0.0001904085,0.004001772,0.001369836,0.05644839],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.04183563,"threshold_uncertainty_score":0.1399541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02316087573093373,"score_gpt":0.231215373945561,"score_spread":0.2080544982146273,"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."}}