{"id":"W2158006286","doi":"10.4314/wsa.v29i4.5040","title":"An efficient optimisation method in groundwater resource management","year":2004,"lang":"en","type":"article","venue":"Water SA","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Groundwater; Aquifer; Groundwater flow; Monte Carlo method; Computer science; Mathematical optimization; Point (geometry); Resource (disambiguation); Operations research; Mathematics; Geology; Statistics; 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":[0.0004277382,0.0001071364,0.00009798316,0.0001576241,0.00002259618,0.00004226287,0.0001085066,0.00005057707,0.00002806988],"category_scores_gemma":[0.000001654097,0.00008500234,0.00002603838,0.00009640902,0.00000522375,0.00007193482,0.00001775499,0.00008945398,0.00003740144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064996,"about_ca_system_score_gemma":0.000001079863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001798123,"about_ca_topic_score_gemma":0.000002155034,"domain_scores_codex":[0.9992919,0.00004410106,0.0001689053,0.0001478469,0.0001210993,0.0002261723],"domain_scores_gemma":[0.9996891,0.000009630859,0.000004977709,0.0002382857,0.000007500658,0.00005048813],"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":[0.000004389536,0.00002106084,0.0000353096,0.00003414767,0.000008169384,0.0000103095,0.001127963,0.9945147,0.00210681,0.0002558079,0.000007014387,0.001874297],"study_design_scores_gemma":[0.0008507664,0.00002490262,0.001896398,0.00002545799,0.000006894315,0.000003312159,0.00008582832,0.9598678,0.03019581,0.0004652884,0.0063692,0.000208354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4776279,0.00001299787,0.5203249,0.00003288717,0.00008361205,0.00008041523,2.560315e-7,0.0001703859,0.001666654],"genre_scores_gemma":[0.7603679,0.000002476586,0.2394029,0.0000163278,0.00003335282,0.00001701014,0.00001531248,0.00002277518,0.000121916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.28274,"threshold_uncertainty_score":0.3466294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266401687866676,"score_gpt":0.2671310609117353,"score_spread":0.2544670440330685,"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."}}