{"id":"W7083416425","doi":"10.2139/ssrn.5519013","title":"Water level prediction of coastal inland river by integrating bimodal decomposition and hybrid deep learning model","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Deep learning; Hyperparameter; Flood myth; Water level; Artificial neural network; Residual; Convolutional neural network; Coastal flood; Deep water","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001344897,0.0002525697,0.0002936426,0.00008104836,0.0003185718,0.00005781429,0.0002342804,0.0001937051,0.00003419569],"category_scores_gemma":[0.0000640528,0.0001918598,0.0001010917,0.00005467951,0.0002110993,0.0001450434,0.0006413776,0.00367055,0.000005479084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007798101,"about_ca_system_score_gemma":0.0001520809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004560675,"about_ca_topic_score_gemma":0.000164286,"domain_scores_codex":[0.9975501,0.000173635,0.0004309778,0.0003943224,0.0003298619,0.001121122],"domain_scores_gemma":[0.9994898,0.000038666,0.0002506061,0.0001151415,0.00002680968,0.00007890968],"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":[0.0001336682,0.0001130095,0.02522683,0.00003559873,0.000157686,0.000003867895,0.0009080194,0.8963628,0.02345693,0.0002406804,0.0001074123,0.05325349],"study_design_scores_gemma":[0.0004993142,0.0003344741,0.0007952437,0.0001423564,0.00009203314,0.0002262807,0.00006154141,0.8962325,0.002343207,0.09897959,0.00005565809,0.000237747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7812667,0.0001448026,0.2177037,0.00009618606,0.00008846405,0.0001057673,0.000046018,0.0000283149,0.0005199636],"genre_scores_gemma":[0.9966792,0.0004516898,0.002049675,0.00002724202,0.00004970641,0.000005317806,0.0001011194,0.00001608846,0.0006200001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2156541,"threshold_uncertainty_score":0.998628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137722526974767,"score_gpt":0.2337023398490549,"score_spread":0.2223251145793073,"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."}}