{"id":"W2172114125","doi":"10.1007/s00477-010-0409-9","title":"Factorial two-stage stochastic programming for water resources management","year":2010,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Water resources management and optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Mathematical optimization; Computer science; Factorial; Stochastic programming; Stage (stratigraphy); Resource allocation; Computational intelligence; Water resources; Factorial experiment; Interval (graph theory); Linear programming; Operations research; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006053799,0.001855055,0.002763078,0.000855189,0.0007831256,0.001818279,0.002110071,0.00367406,0.005692116],"category_scores_gemma":[0.01085373,0.001722068,0.002183482,0.001320244,0.00146962,0.001933846,0.001736556,0.002508307,0.0004975823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00260479,"about_ca_system_score_gemma":0.002793071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199766,"about_ca_topic_score_gemma":0.01153148,"domain_scores_codex":[0.9972649,0.001718194,0.00009578133,0.0002863082,0.0003331449,0.0003017498],"domain_scores_gemma":[0.9935469,0.005500529,0.000282307,0.0001111536,0.000364239,0.0001948958],"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":[0.00008245935,0.00004658007,0.0001635508,0.00006911289,0.00003599999,0.00003622895,0.00001867445,0.9686075,0.0005002815,0.02431248,0.0005738075,0.005553284],"study_design_scores_gemma":[0.000008620662,0.00001963018,0.00003517996,0.000002251489,0.000007091664,0.000002343458,0.000001870208,0.9950596,0.00007779156,0.004613888,0.0001647999,0.000006882272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01067719,0.0006231164,0.9851223,0.0003972409,0.0001330188,0.0000713015,0.0001463373,0.0001852115,0.002644264],"genre_scores_gemma":[0.6662455,0.00130172,0.308885,0.0004399606,0.0003664056,0.000919028,0.0005418711,0.0002819909,0.0210186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01199766,"threshold_uncertainty_score":0.03201598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347861944493667,"score_gpt":0.2840732148144564,"score_spread":0.2705945953695197,"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."}}