{"id":"W1603639300","doi":"10.1109/ccece.2001.933604","title":"A robust model parameter extraction technique based on meta-evolutionary programming for high speed/high frequency package interconnects","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Coplanar waveguide; Parametric statistics; Electronic engineering; Flip chip; Stripline; Evolutionary algorithm; Terahertz radiation; Convergence (economics); Algorithm; Engineering; Optoelectronics; Mathematics; Materials science; Telecommunications; Artificial intelligence","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":[0.0006789853,0.0007271708,0.0007102861,0.000443587,0.0003305316,0.0005474649,0.0008212012,0.001010488,0.0008022231],"category_scores_gemma":[0.0010755,0.0004679115,0.0009252791,0.0005654904,0.0004416252,0.0005181422,0.000528394,0.001002735,0.0001521855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003337777,"about_ca_system_score_gemma":0.0005567473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007938308,"about_ca_topic_score_gemma":0.0006715473,"domain_scores_codex":[0.9997589,0.00009106475,0.000009667651,0.00003627522,0.0000836429,0.00002046253],"domain_scores_gemma":[0.9997781,0.0001118643,0.00003933311,0.00002694301,0.00003529737,0.000008526044],"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":[0.00001453236,0.00002268074,0.0002009519,0.00003893817,0.00006131169,0.00005597671,0.00002764602,0.9447807,0.006892369,0.009182445,0.0002210711,0.03850131],"study_design_scores_gemma":[0.000007948481,0.00002810388,0.00007769625,0.000005484308,0.00001137679,0.00003681717,0.00000460676,0.9945078,0.001595088,0.003019193,0.0007006002,0.000005341255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008176754,0.0001177397,0.9908506,0.00005679231,0.00001009175,0.00001481742,0.00001089075,0.00009915698,0.0006630189],"genre_scores_gemma":[0.2888471,0.0002412126,0.7089004,0.00005577525,0.00002209878,0.0002543131,0.00007277989,0.00007387019,0.001532532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001010488,"threshold_uncertainty_score":0.003590882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07434086611109436,"score_gpt":0.278111755652354,"score_spread":0.2037708895412596,"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."}}