{"id":"W2563331167","doi":"10.1002/2016wr019573","title":"Improving operating policies of large‐scale surface‐groundwater systems through stochastic programming","year":2016,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Water resources management and optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"European Commission","keywords":"Aquifer; Groundwater; Stochastic programming; Mathematical optimization; Computer science; Scale (ratio); Surface water; Water resources; Resource (disambiguation); Environmental science; Geology; Mathematics; Environmental engineering; Geotechnical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001147123,0.0002324788,0.0002972744,0.0002521067,0.0002797675,0.0003564133,0.0004616589,0.0001129789,0.00007025311],"category_scores_gemma":[0.00003163331,0.0001329777,0.00007053413,0.0003007219,0.0001637788,0.0004327501,0.0003932184,0.0002216594,0.0001022303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001155769,"about_ca_system_score_gemma":0.000005159735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005448555,"about_ca_topic_score_gemma":0.00003633496,"domain_scores_codex":[0.9971319,0.0001922991,0.0004697231,0.0003354912,0.000731233,0.001139336],"domain_scores_gemma":[0.9991748,0.00008981036,0.00003793318,0.0004216448,0.0001817157,0.00009403873],"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":[0.00008855491,0.0001988141,0.004483113,0.002071349,0.0002738347,0.00002221457,0.0873088,0.6078575,0.2896364,0.0002499582,0.0007181024,0.007091387],"study_design_scores_gemma":[0.004242797,0.0008686506,0.0005880053,0.001804852,0.00009240877,0.00002528464,0.01916008,0.5711316,0.232896,0.0001967186,0.1671052,0.001888385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559441,0.0003143728,0.04090807,0.0001236295,0.0001379176,0.0006790849,0.000008878976,0.0002849149,0.001599028],"genre_scores_gemma":[0.9947905,0.00001790623,0.001054334,0.000005040886,0.0001983268,0.00006431694,0.00001487754,0.00008867399,0.003766013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1663871,"threshold_uncertainty_score":0.5422673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907389709249531,"score_gpt":0.2803453307679443,"score_spread":0.251271433675449,"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."}}