{"id":"W2988768477","doi":"10.1088/1755-1315/344/1/012002","title":"Maximum entropy method for flood frequency analysis: A case study of the Grand River in Ontario, Canada","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Flood myth; Akaike information criterion; Principle of maximum entropy; Bayesian information criterion; Probability distribution; Statistics; Entropy (arrow of time); Hydrology (agriculture); Frequency distribution; 100-year flood; Generalized extreme value distribution; Extreme value theory; Mathematics; Computer science; Geography; Geology; Geotechnical engineering; Physics","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":[0.0006749996,0.0002619737,0.0002271252,0.001445491,0.001354242,0.0007278593,0.0006811646,0.0003944463,0.001054974],"category_scores_gemma":[0.002160928,0.0001262657,0.0002493459,0.002246192,0.000607991,0.0003497297,0.0003890323,0.0002369956,0.00006267815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007910262,"about_ca_system_score_gemma":0.005875799,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9022554,"about_ca_topic_score_gemma":0.9397404,"domain_scores_codex":[0.9996266,0.00008859696,0.00001713094,0.00003970862,0.0001667647,0.00006114908],"domain_scores_gemma":[0.9992425,0.000404446,0.00004863096,0.00003256209,0.0002316173,0.00004024346],"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":[0.0002582664,0.0002140127,0.3027412,0.0004251266,0.0001448095,0.006556249,0.004799558,0.3949604,0.008007606,0.02119015,0.006880165,0.2538225],"study_design_scores_gemma":[0.00002723369,0.00004872756,0.2513034,0.00005179934,0.00005877257,0.0005510002,0.004462568,0.7279127,0.003168984,0.004962903,0.007368809,0.00008317114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9537534,0.0003411003,0.03497435,0.0005632488,0.000009256702,0.0001323817,0.001147161,0.0001346558,0.008944533],"genre_scores_gemma":[0.9835397,0.000132609,0.01363422,0.00001195497,0.000004203262,0.00002195289,0.0003098174,0.00001273678,0.00233272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09774464,"threshold_uncertainty_score":0.1966405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007924510775751752,"score_gpt":0.2045341510292088,"score_spread":0.196609640253457,"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."}}