{"id":"W4315433092","doi":"10.1088/1748-9326/acb1c8","title":"Storm surge variability and prediction from ENSO and tropical cyclones","year":2023,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Tropical and Extratropical Cyclones Research","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering; Government of Jiangsu Province; Sight Research UK; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Natural Science Foundation of China; Natural Environment Research Council; Met Office","keywords":"Storm surge; Tropical cyclone; Climatology; Storm; Environmental science; El Niño Southern Oscillation; Forcing (mathematics); Surge; Natural hazard; Meteorology; Geology; 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":[0.0004548898,0.0001207064,0.0001486768,0.0001095746,0.0003551742,0.0000972488,0.0001424685,0.00008387941,0.00159196],"category_scores_gemma":[0.0001449953,0.00009626195,0.00003366695,0.000192814,0.0008277168,0.0001982927,0.0001103527,0.0004747293,0.0004921271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002237504,"about_ca_system_score_gemma":0.000009052792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002604662,"about_ca_topic_score_gemma":0.0005174727,"domain_scores_codex":[0.9974734,0.0004554266,0.0001710051,0.0004972463,0.0008087275,0.0005942057],"domain_scores_gemma":[0.998376,0.00103306,0.000013603,0.0001901339,0.000003868256,0.0003833721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008383462,0.00001936418,0.9650457,0.00000829298,0.00001219975,0.00004659337,0.00008222274,0.00001657146,0.004332926,0.00001841297,0.0006281958,0.02970569],"study_design_scores_gemma":[0.0002584767,0.0001207761,0.9918866,0.000004976601,0.000003140382,0.000003993817,0.0001059623,0.001757136,0.00008563653,0.0008230155,0.004857416,0.0000929426],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952048,0.0001882419,0.00003346986,0.003846831,0.00008542093,0.0001983911,0.0002978053,0.00005244507,0.00009260587],"genre_scores_gemma":[0.9985708,0.000715701,0.000120037,0.0001363654,0.0002009094,0.000005972974,0.0001852259,0.000005242679,0.00005975437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02961275,"threshold_uncertainty_score":0.9993207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02791053555734575,"score_gpt":0.2540600698278235,"score_spread":0.2261495342704778,"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."}}