{"id":"W2063341969","doi":"10.1007/s11269-010-9668-y","title":"Selecting Model Parameter Sets from a Trade-off Surface Generated from the Non-Dominated Sorting Genetic Algorithm-II","year":2010,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture","keywords":"Parameter space; Mathematical optimization; Sorting; Genetic algorithm; Calibration; Set (abstract data type); Pareto principle; Solution set; Algorithm; Multi-objective optimization; Computer science; Selection (genetic algorithm); Surface (topology); Mathematics; Data mining; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004638738,0.0003846941,0.0002895973,0.00003765849,0.001086306,0.0001329419,0.0007430506,0.0001233732,0.0006372556],"category_scores_gemma":[0.000008698686,0.0002404091,0.0001027209,0.000179864,0.0002570204,0.0001486988,0.001593082,0.0004221533,0.0003696942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005361655,"about_ca_system_score_gemma":0.000001263111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00148874,"about_ca_topic_score_gemma":0.0003627439,"domain_scores_codex":[0.997399,0.0001318408,0.0004446754,0.0008395145,0.0004057317,0.0007792361],"domain_scores_gemma":[0.9990773,0.00007408013,0.0001320142,0.0006168354,0.000005985637,0.0000938396],"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":[0.0002105761,0.0006038119,0.09662325,0.00003466687,0.002223077,0.0003278014,0.06642772,0.6017906,0.07836492,0.00001074638,0.01837924,0.1350036],"study_design_scores_gemma":[0.001133161,0.00006541498,0.03964121,0.00001839272,0.000337741,0.000001843953,0.0005377901,0.9210418,0.0101541,0.002219886,0.02421842,0.0006302234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905529,0.00003940886,0.00388173,0.001901356,0.0002327823,0.0006430337,0.00001639622,0.0001266945,0.002605681],"genre_scores_gemma":[0.9730864,0.00002992288,0.02333577,0.001430951,0.00008978118,0.00006856958,0.00006735449,0.00004498722,0.0018463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3192512,"threshold_uncertainty_score":0.98036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00820584903122235,"score_gpt":0.2039387879472983,"score_spread":0.1957329389160759,"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."}}