{"id":"W4403578714","doi":"10.1007/s11069-025-07428-4","title":"Advancing spatio-temporal storm surge prediction with hierarchical deep neural networks","year":2025,"lang":"en","type":"preprint","venue":"Natural Hazards","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Storm surge; Storm; Artificial neural network; Computer science; Artificial intelligence; Climatology; Geography; Meteorology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003445246,0.0005678875,0.0006408884,0.0003840397,0.0002692888,0.0005743817,0.001077048,0.0008141599,0.001979495],"category_scores_gemma":[0.001926667,0.0006244536,0.0005001826,0.0004915253,0.0003346494,0.0008356069,0.000827108,0.001266049,0.0003313254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006926325,"about_ca_system_score_gemma":0.0009221524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03546779,"about_ca_topic_score_gemma":0.0415757,"domain_scores_codex":[0.9999155,0.00001643802,0.000004488978,0.00002328861,0.00001749436,0.00002278926],"domain_scores_gemma":[0.9994809,0.000309621,0.00004472061,0.00004357132,0.00007486856,0.00004647564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003683566,0.00003066391,0.0008855913,0.00001252078,0.00002573158,0.00001843912,0.000009723539,0.9873562,0.0003179249,0.0009796419,0.0008385637,0.009488191],"study_design_scores_gemma":[0.000001179847,0.000001000789,0.00003267813,3.318669e-7,6.150339e-7,3.420686e-7,5.775676e-7,0.9995953,0.0000174297,0.000336611,0.00001368283,3.108205e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4746175,0.001064958,0.511176,0.001581323,0.0003271977,0.00004943886,0.001794451,0.002651592,0.006737344],"genre_scores_gemma":[0.9730531,0.0001133172,0.02420599,0.0001189588,0.0000897901,0.00002763754,0.0007461057,0.00006169371,0.001583322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03546779,"threshold_uncertainty_score":0.07052273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01066175036539318,"score_gpt":0.2331850823419428,"score_spread":0.2225233319765496,"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."}}