{"id":"W2298639746","doi":"10.14288/1.0050419","title":"A level set global optimization method for nonlinear engineering problems","year":2009,"lang":"en","type":"article","venue":"Open Collections","topic":"Water resources management and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Set (abstract data type); Nonlinear programming; Nonlinear system; Computer science; Mathematical optimization; Point (geometry); Reliability (semiconductor); Convergence (economics); Function (biology); Mathematics; Programming language","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":[0.0001097659,0.0001117594,0.0001108285,0.0000500622,0.0003696928,0.0006911511,0.0001743602,0.0000489886,0.00004689703],"category_scores_gemma":[0.00001786574,0.000123716,0.00003957199,0.0007150904,0.000002637061,0.0001858476,0.00002996181,0.00004258482,0.000002022892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009158629,"about_ca_system_score_gemma":0.00001078381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008591413,"about_ca_topic_score_gemma":0.00007561323,"domain_scores_codex":[0.9994612,0.000008921092,0.0001508153,0.000144128,0.00006433936,0.0001705825],"domain_scores_gemma":[0.9997615,0.00001215133,0.00002016968,0.000118749,0.00004534732,0.00004212281],"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":[0.000006428459,0.0000162276,0.000001855997,0.00001449694,0.00002630204,1.67641e-7,0.0000406884,0.9484293,0.00001452289,0.00005576052,0.0506639,0.0007303632],"study_design_scores_gemma":[0.0004479239,0.00005650415,0.00001348709,0.00001396995,0.00002545573,0.000002641421,0.00001403201,0.9262851,0.0000908616,0.0001311335,0.07277922,0.0001396097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001415593,0.00001690626,0.9605131,0.00009105866,0.0001523511,0.00093996,0.0000843293,0.000210823,0.03797728],"genre_scores_gemma":[0.0008683802,0.00002021468,0.9755426,0.00006060384,0.0001212493,0.0002145129,0.0002305108,0.00002804398,0.02291385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02214411,"threshold_uncertainty_score":0.6664786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901359256184152,"score_gpt":0.2650451400337341,"score_spread":0.2360315474718926,"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."}}