{"id":"W4413442076","doi":"10.1111/jfr3.70114","title":"Integrated Machine Learning and Hydrodynamic Modeling for Agricultural Land Flood Under Climate Change Scenarios","year":2025,"lang":"en","type":"article","venue":"Journal of Flood Risk Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Climate change; Flood myth; Environmental science; Agriculture; Land use, land-use change and forestry; Hydrology (agriculture); Land use; Environmental resource management; Geography; Geology; Engineering; Civil engineering; Geotechnical engineering; Oceanography; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005610449,0.0005127997,0.0004587524,0.0006702381,0.0004151272,0.0005841497,0.0005007323,0.0009123225,0.001246208],"category_scores_gemma":[0.00120018,0.0003173671,0.0006736986,0.000619203,0.0003080237,0.0006723166,0.0004349673,0.0005853092,0.0001227351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227678,"about_ca_system_score_gemma":0.0008736398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03492774,"about_ca_topic_score_gemma":0.02080598,"domain_scores_codex":[0.9998228,0.0000625497,0.00001036285,0.00003537774,0.000034992,0.00003388582],"domain_scores_gemma":[0.9995313,0.0002689284,0.00004911189,0.00002309205,0.00009667353,0.00003095482],"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":[0.00001909147,0.00003321841,0.00263925,0.000004238143,0.00001259371,0.0000211383,0.000005790673,0.9955804,0.0001696805,0.0001200302,0.00006591808,0.001328636],"study_design_scores_gemma":[0.000002319027,0.000006596781,0.0006531586,4.172993e-7,0.000001773856,0.000001040255,0.000002487816,0.9991866,0.00005572501,0.00006240202,0.00002593627,0.000001649007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794612,0.00007214094,0.01637446,0.0001994563,0.00003111146,0.00004863345,0.0004033491,0.0002422484,0.00316751],"genre_scores_gemma":[0.9966133,0.0000293973,0.002540092,0.00001274604,0.000007653663,0.00003108096,0.0001722985,0.000006634238,0.0005868144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03492774,"threshold_uncertainty_score":0.06944883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00856524922843056,"score_gpt":0.2376833096117756,"score_spread":0.229118060383345,"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."}}