{"id":"W1980906693","doi":"10.1016/j.compchemeng.2005.04.005","title":"A robust optimization of injection molding runner balancing","year":2005,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Sensitivity (control systems); Molding (decorative); Process (computing); Robust optimization; Volume (thermodynamics); Computer science; Optimization problem; Mathematics; Engineering; Mechanical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009820893,0.001050222,0.001584906,0.0005231787,0.0004489438,0.001035633,0.0009111015,0.001448484,0.004608619],"category_scores_gemma":[0.001721617,0.0008568807,0.0007494275,0.0003816503,0.000577185,0.0007217598,0.0009746504,0.0006756152,0.0005882852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006080912,"about_ca_system_score_gemma":0.0009548313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003664574,"about_ca_topic_score_gemma":0.002432964,"domain_scores_codex":[0.9996924,0.00009583573,0.00001212945,0.00007644364,0.0000875434,0.00003560952],"domain_scores_gemma":[0.9996713,0.0001569042,0.00005967374,0.00002928896,0.00006008902,0.00002274827],"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":[0.00006625573,0.00001845676,0.00008381357,0.00004034095,0.00002222972,0.00002159576,0.00001062449,0.9827907,0.002650037,0.003000831,0.0003125316,0.01098273],"study_design_scores_gemma":[0.000007820767,0.00003011268,0.00003797064,0.000002161859,0.000004187073,0.000003201758,0.000001793757,0.9988241,0.0003833256,0.0004882452,0.0002140839,0.000003069677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02998677,0.000268035,0.9598461,0.0001805663,0.00009745662,0.0000887604,0.00008345961,0.0004567057,0.008992157],"genre_scores_gemma":[0.802299,0.000191938,0.1880218,0.0001048391,0.00007452631,0.0001841299,0.0001872243,0.0002904339,0.008646156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004608619,"threshold_uncertainty_score":0.0154174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005977475696652146,"score_gpt":0.1653649011983508,"score_spread":0.1593874255016987,"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."}}