{"id":"W4236276892","doi":"10.24124/2010/bpgub681","title":"ENSO ensemble prediction and predictability for the past 148 years from 1856--2003.","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences; University of Northern British Columbia","keywords":"Predictability; Hindcast; El Niño Southern Oscillation; Climatology; Probabilistic logic; Forecast skill; Ensemble forecasting; Ensemble learning; Econometrics; Meteorology; Environmental science; Computer science; Geography; Artificial intelligence; Mathematics; Statistics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0002695935,0.0001397654,0.00007339013,0.0002807214,0.0001425857,0.0001858334,0.0001128096,0.0001215706,0.0006376262],"category_scores_gemma":[0.0007839597,0.00005814447,0.0001940762,0.0005354122,0.00006165736,0.0002294242,0.0001527771,0.000260488,0.0002952331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004347358,"about_ca_system_score_gemma":0.0003340521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02837888,"about_ca_topic_score_gemma":0.04611527,"domain_scores_codex":[0.9999492,0.000008121898,0.000003721726,0.00001304166,0.00001867427,0.000007257346],"domain_scores_gemma":[0.9998828,0.00001577237,0.00002617235,0.0000150248,0.00005012644,0.00000992949],"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":[0.0004300078,0.0001322993,0.3274143,0.0001757851,0.0003565096,0.000226056,0.0003446114,0.2084039,0.002114463,0.009088919,0.1508538,0.3004594],"study_design_scores_gemma":[0.00002509339,0.00005146658,0.7041778,0.00009678178,0.000146587,0.0001314389,0.0001222218,0.1713138,0.00260034,0.005765675,0.1155481,0.00002067988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8799776,0.007912221,0.01535037,0.003822903,0.00167642,0.0000297311,0.0624234,0.0003837287,0.02842371],"genre_scores_gemma":[0.9714991,0.001583091,0.003350481,0.00005015109,0.0001639899,0.00001291294,0.01833942,0.00004295496,0.004957886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02837888,"threshold_uncertainty_score":0.05642736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148178949234706,"score_gpt":0.233091096929308,"score_spread":0.2216093074369609,"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."}}