{"id":"W2120949859","doi":"10.1061/(asce)wr.1943-5452.0000492","title":"Inexact Probabilistic Optimization Model and Its Application to Flood Diversion Planning in a Dynamic and Uncertain Environment","year":2014,"lang":"en","type":"article","venue":"Journal of Water Resources Planning and Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Probabilistic logic; Flood myth; Mathematical optimization; Variety (cybernetics); Stochastic programming; Dynamic programming; Computer science; Function (biology); Operations research; Boundary (topology); Dual (grammatical number); Flood risk management; Mathematics; Artificial intelligence","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":[0.001237494,0.0007868062,0.001010032,0.0004588416,0.0003664049,0.001126779,0.001099497,0.001146522,0.001368443],"category_scores_gemma":[0.003195191,0.0006689187,0.000797197,0.000674134,0.0008800115,0.001008887,0.0008556141,0.001446153,0.0001166943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007672149,"about_ca_system_score_gemma":0.001235704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007238645,"about_ca_topic_score_gemma":0.003775709,"domain_scores_codex":[0.9993657,0.0002902818,0.00002755696,0.00008991687,0.0001609838,0.0000656576],"domain_scores_gemma":[0.9985321,0.001005299,0.0002432226,0.00005159302,0.000124083,0.00004379745],"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":[0.000005405221,0.000002744148,0.00005025151,0.000007269172,0.000003507473,0.0000159441,0.00000449965,0.9964901,0.0000780618,0.002327463,0.00002892063,0.0009858508],"study_design_scores_gemma":[9.068534e-7,0.000004583741,0.00001646448,8.376447e-7,0.000001446592,0.000003547158,0.00000114857,0.999289,0.00003726872,0.0005950135,0.00004837884,0.000001248968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02379586,0.0002136255,0.9730909,0.000229439,0.00002406906,0.00002576072,0.00005518907,0.0001006232,0.002464474],"genre_scores_gemma":[0.8760671,0.000580455,0.1195302,0.00007582378,0.00004112074,0.0001721357,0.0001002127,0.00004369735,0.003389181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007238645,"threshold_uncertainty_score":0.01439303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007074936027263021,"score_gpt":0.1929693459982697,"score_spread":0.1858944099710067,"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."}}