{"id":"W2986801453","doi":"10.1007/s00170-019-04506-3","title":"A new ensemble modeling approach for reliability-based design optimization of flexure-based bridge-type amplification mechanisms","year":2019,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Bridge (graph theory); Reliability (semiconductor); Ensemble forecasting; Computer science; Process (computing); Surrogate model; Model selection; Selection (genetic algorithm); Ensemble learning; Data mining; Mathematical optimization; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001652968,0.001408909,0.002091547,0.001264796,0.0006831595,0.001255663,0.001957373,0.001872528,0.002574578],"category_scores_gemma":[0.002236581,0.001075198,0.002359386,0.001087281,0.0005258291,0.001450587,0.001417743,0.001516307,0.0004494922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006724221,"about_ca_system_score_gemma":0.001110266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00491096,"about_ca_topic_score_gemma":0.005833127,"domain_scores_codex":[0.9993987,0.0002006659,0.00003078715,0.00009593905,0.0002061128,0.00006781695],"domain_scores_gemma":[0.9989925,0.0004958379,0.000127573,0.00009605072,0.0002349696,0.00005312205],"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":[0.000005399257,0.00001316477,0.0001086048,0.000009835915,0.00002447254,0.00001056356,0.000005807724,0.9935743,0.0003710524,0.001807081,0.0001031757,0.003966483],"study_design_scores_gemma":[6.291982e-7,0.000004179538,0.0000176308,0.000001239542,0.000003466351,0.000001768864,7.753166e-7,0.9993027,0.00003886554,0.0005555328,0.00007193019,0.000001280334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01024733,0.0002105253,0.9868574,0.00008065286,0.00003704782,0.00002807052,0.00007204827,0.0001313833,0.002335649],"genre_scores_gemma":[0.6393018,0.0009089251,0.3511086,0.0002528364,0.0001862998,0.0005857173,0.0005690724,0.0002724901,0.006814342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00491096,"threshold_uncertainty_score":0.009764731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06290486589909511,"score_gpt":0.3124923017691075,"score_spread":0.2495874358700124,"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."}}