{"id":"W1969422736","doi":"10.1109/tmech.2011.2165958","title":"Mechatronic Design Evolution Using Bond Graphs and Hybrid Genetic Algorithm With Genetic Programming","year":2011,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Mechatronics Education and Applications","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mechatronics; Realization (probability); Topology (electrical circuits); Computer science; Genetic programming; Genetic algorithm; Network topology; Feature (linguistics); Process (computing); Control engineering; Algorithm; Artificial intelligence; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001869144,0.0004413097,0.0002977278,0.0003761308,0.0004029444,0.00007664372,0.0002463253,0.0001523158,0.0001049375],"category_scores_gemma":[0.000001768792,0.0004555994,0.0001141605,0.0005080764,0.00009781246,0.0002110034,0.000003940674,0.0004861538,0.00004232217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978987,"about_ca_system_score_gemma":0.0002133722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005779743,"about_ca_topic_score_gemma":0.00002832687,"domain_scores_codex":[0.9979337,0.00006023767,0.00043375,0.0005487379,0.000317004,0.0007066012],"domain_scores_gemma":[0.9988825,0.00004412513,0.00009411535,0.0006029207,0.00009303696,0.0002833016],"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":[0.0001086234,0.000904405,0.00004237438,0.0002512827,0.0007387609,0.00002144242,0.001583578,0.5637383,0.007964985,0.01590958,0.0001376641,0.408599],"study_design_scores_gemma":[0.001413044,0.0006850108,0.00014875,0.0001099487,0.0006098234,0.0003775009,0.0007246251,0.9455759,0.03313414,0.01483579,0.001123822,0.001261637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04874916,0.001126413,0.9481532,0.00002133714,0.0003493315,0.0009381484,0.0000323877,0.0005658827,0.00006411772],"genre_scores_gemma":[0.5602316,0.0003479612,0.4389735,0.00001717721,0.0000267934,0.0002685182,0.000004864221,0.00009715604,0.00003239229],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5114825,"threshold_uncertainty_score":0.9997896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325263451314594,"score_gpt":0.2123519062753794,"score_spread":0.1890992717622335,"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."}}