{"id":"W2139541110","doi":"10.1109/tmag.2006.892106","title":"Evolution of Two-Dimensional Electromagnetic Devices Using a Novel Genome Structure","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Genetic algorithm; Evolutionary algorithm; Dual (grammatical number); Evolutionary computation; Antenna (radio); Artificial intelligence; Telecommunications; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00007168591,0.0001514281,0.0001345804,0.0002223266,0.00006997221,0.00001092554,0.00007776435,0.000100382,0.0002064919],"category_scores_gemma":[0.000001282523,0.0001630968,0.00005526945,0.0003529259,0.0000462811,0.00007445908,4.972169e-7,0.0001761661,0.000004597363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000120395,"about_ca_system_score_gemma":0.00002924715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002300722,"about_ca_topic_score_gemma":0.00008908972,"domain_scores_codex":[0.9991671,0.000009920075,0.0002621602,0.0001331636,0.0001907867,0.0002368175],"domain_scores_gemma":[0.9996355,0.00003804289,0.00003842207,0.0001410936,0.00008443413,0.0000625137],"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":[0.00001399975,0.00002434555,0.000005522459,0.00001563551,0.000009854516,6.025401e-7,0.00003235511,0.523937,0.4755046,0.00002823859,0.000001038259,0.0004268172],"study_design_scores_gemma":[0.000717347,0.0002808954,0.001611234,0.00003056531,0.000112638,0.00004175179,0.00003607299,0.9136539,0.08311142,0.0001118209,0.00002733246,0.0002650805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3015136,0.0002109432,0.6977646,0.000003329665,0.0002048787,0.0001018518,0.00002626871,0.00007426018,0.0001002173],"genre_scores_gemma":[0.9348296,0.00001054948,0.06501395,0.0000183584,0.00004176524,0.000001160567,0.000003924932,0.00003049748,0.00005021016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.633316,"threshold_uncertainty_score":0.6650894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016938704751074,"score_gpt":0.2150011155695124,"score_spread":0.2048317285220017,"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."}}