{"id":"W2054944549","doi":"10.1016/j.advwatres.2008.01.010","title":"Comparison of derivative-free optimization methods for groundwater supply and hydraulic capture community problems","year":2008,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Water Systems and Optimization","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Sandia National Laboratories; Army Research Office; National Institute of Environmental Health Sciences; North Carolina State University; National Nuclear Security Administration; National Institutes of Health; National Science Foundation","keywords":"Maxima and minima; Mathematical optimization; Groundwater; Computer science; Water supply; Optimization problem; Mathematics; Environmental science; Engineering; Environmental 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":[0.0002701666,0.0001537178,0.0003174342,0.0001126527,0.0001247241,0.00002021302,0.000190737,0.00008189295,0.000005908982],"category_scores_gemma":[0.00002096162,0.0001126911,0.00002889276,0.00009636883,0.00009541287,0.0004025608,0.00006596519,0.0001409898,3.092569e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002407633,"about_ca_system_score_gemma":0.000001447174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001371887,"about_ca_topic_score_gemma":0.0002823996,"domain_scores_codex":[0.9990664,0.0001558993,0.0003729452,0.0001242477,0.00007733055,0.0002031804],"domain_scores_gemma":[0.9995426,0.0000955794,0.00005357824,0.0002313104,0.00004572291,0.00003123436],"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":[0.00001603975,0.000033631,0.009237713,0.0003388026,0.00001283669,2.819324e-7,0.02895632,0.9596967,0.0006728395,0.000008310742,0.0000527974,0.0009737987],"study_design_scores_gemma":[0.00166458,0.0002496708,0.001615885,0.0002333065,0.00002330581,0.00001956446,0.001498819,0.8495955,0.07884213,0.0009540837,0.06482425,0.0004789504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3681592,0.004018047,0.6263474,0.0000427638,0.000120951,0.0004754162,0.000005987515,0.00008908352,0.0007411514],"genre_scores_gemma":[0.8950697,0.0002509594,0.1044031,0.00001065753,0.00002365439,0.0000744893,0.00005181132,0.00002879851,0.00008682783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5269105,"threshold_uncertainty_score":0.4595408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02573503127780425,"score_gpt":0.2960509478138718,"score_spread":0.2703159165360675,"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."}}