{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001591752,0.0006560142,0.0007541717,0.0004845233,0.0003135994,0.001237568,0.0006651905,0.0006115964,0.00109216],"category_scores_gemma":[0.002539702,0.0004399662,0.0005115126,0.0005276622,0.0008017095,0.0008119391,0.0009511739,0.0007465503,0.00008072195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478683,"about_ca_system_score_gemma":0.001892937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01003207,"about_ca_topic_score_gemma":0.005811008,"domain_scores_codex":[0.9993541,0.0003350484,0.00002011525,0.00008452563,0.00008473451,0.0001215442],"domain_scores_gemma":[0.9984694,0.001004255,0.0002412794,0.00004781319,0.0001382804,0.00009897132],"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":[0.00001129787,0.000007779935,0.0001354951,0.000004238697,0.000005593655,0.000005567519,0.000002565626,0.9976637,0.000139098,0.0009517861,0.00003711168,0.001035765],"study_design_scores_gemma":[0.000002925688,0.00000923089,0.00004600732,7.889889e-7,0.000001587217,6.408375e-7,0.00000270523,0.9989254,0.00007312225,0.0009068411,0.00002974044,9.458905e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2869437,0.0002113762,0.7076722,0.000535272,0.00002690291,0.0000803364,0.0001366873,0.0002836814,0.004109856],"genre_scores_gemma":[0.9874049,0.00007026866,0.01196497,0.00002082221,0.000005896364,0.0000380787,0.00003005292,0.00001428248,0.0004506579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01003207,"threshold_uncertainty_score":0.01994735,"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."}}