{"id":"W2088744342","doi":"10.1007/s00158-006-0061-7","title":"An adaptive approach to constraint aggregation using adjoint sensitivity analysis","year":2006,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Constraint (computer-aided design); Sensitivity (control systems); Mathematical optimization; Key (lock); Minification; Computer science; Function (biology); Mathematics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001978823,0.0002797343,0.0003032224,0.0004102505,0.0005662208,0.00017718,0.0001651621,0.00009426112,0.000004667062],"category_scores_gemma":[0.00002203123,0.0002588768,0.000079251,0.001401297,0.0001175597,0.001455973,0.0001976443,0.0001129341,9.081137e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001445734,"about_ca_system_score_gemma":0.00004536806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001644708,"about_ca_topic_score_gemma":0.00003663152,"domain_scores_codex":[0.9980897,0.0001840763,0.0003365646,0.0008248288,0.0002634927,0.0003013628],"domain_scores_gemma":[0.9988678,0.00004431012,0.0001964544,0.0003771075,0.0003405163,0.0001738756],"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":[0.00002504759,0.00004378044,0.0005745515,0.000005413999,0.0000388724,0.000004959535,0.0005627148,0.9893987,0.0009435277,0.004640964,7.125531e-7,0.003760815],"study_design_scores_gemma":[0.0003888783,0.00009057848,0.01641406,0.00000722732,0.0001052808,0.00005278708,0.0002716566,0.9811743,0.0005025234,0.0006395694,5.015593e-7,0.0003526699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08088415,0.00001990318,0.9180068,0.00005692278,0.0001238812,0.0004688375,0.00002997986,0.0001844614,0.0002250897],"genre_scores_gemma":[0.4885723,0.000001773103,0.511236,0.00001529761,0.00004824673,0.00000728764,0.00009827042,0.00000871578,0.00001215177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4076882,"threshold_uncertainty_score":0.9999864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564829907526832,"score_gpt":0.270565997613664,"score_spread":0.2549176985383956,"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."}}