{"id":"W2049743496","doi":"10.1007/s00158-014-1219-3","title":"Efficient adaptive response surface method using intelligent space exploration strategy","year":2015,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latin hypercube sampling; Computer science; Mathematical optimization; Robustness (evolution); Adaptive sampling; Global optimization; Benchmark (surveying); Algorithm; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005478447,0.0003086786,0.0002490436,0.0001629803,0.0004200687,0.0001654613,0.0002641191,0.0001092997,0.000006516139],"category_scores_gemma":[0.0001298175,0.0002743886,0.00004743638,0.0006770023,0.00009586121,0.001089609,0.0004007545,0.0001618597,0.000004122498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000271409,"about_ca_system_score_gemma":0.0001735293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002949952,"about_ca_topic_score_gemma":0.000003124403,"domain_scores_codex":[0.9977928,0.0004601333,0.000361311,0.0006968839,0.0003642903,0.0003245665],"domain_scores_gemma":[0.9984463,0.0001423023,0.0002467851,0.0003462046,0.0005511154,0.0002672242],"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":[0.0003199551,0.00002291456,0.00002554624,0.000005674817,0.00001226274,0.000008939393,0.003600798,0.9904354,0.0008733546,0.00188389,0.000003322242,0.002808004],"study_design_scores_gemma":[0.0007928925,0.0002617587,0.0002318104,0.00001927172,0.00001902065,0.00006321939,0.002157067,0.9929522,0.001828739,0.001318085,0.000004614446,0.0003513623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06095986,0.0001627559,0.9373067,0.0003047118,0.0004731393,0.0005262665,0.0000103544,0.0001897901,0.00006643232],"genre_scores_gemma":[0.2870634,0.00001313413,0.7127274,0.00001226547,0.00004013886,0.000006976149,0.00001661134,0.00001879124,0.0001012281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2261035,"threshold_uncertainty_score":0.9999709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0666897497182106,"score_gpt":0.342705415130482,"score_spread":0.2760156654122714,"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."}}