{"id":"W4388079833","doi":"10.1109/pimrc56721.2023.10293837","title":"Digital Twin Model Selection for Feature Accuracy in Wireless Edge Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Wireless; Rounding; Relaxation (psychology); Algorithm; Integer (computer science); Selection (genetic algorithm); Channel (broadcasting); Implementation; Feature (linguistics); Mathematical optimization; Artificial intelligence; Computer network; Mathematics; Telecommunications","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":[0.001901615,0.001092748,0.001231132,0.0006361778,0.0005596131,0.001292328,0.001371162,0.0007356198,0.00196483],"category_scores_gemma":[0.006246567,0.0005550031,0.0007677821,0.001137516,0.000730862,0.0022853,0.001475855,0.001330636,0.0002463216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001171808,"about_ca_system_score_gemma":0.001168797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003747725,"about_ca_topic_score_gemma":0.00356458,"domain_scores_codex":[0.9987168,0.000515808,0.00005524475,0.0001789673,0.0003274422,0.0002057905],"domain_scores_gemma":[0.9978495,0.001502624,0.0001823561,0.00018877,0.0001955088,0.00008126735],"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":[0.00006708677,0.00002503564,0.0003087101,0.00002899235,0.000009019169,0.00004488826,0.00002916985,0.9792743,0.0007450678,0.006229741,0.0004259795,0.01281185],"study_design_scores_gemma":[0.00000526512,0.00002216665,0.0000361351,0.000002783142,0.000004049338,0.00001610916,0.00001177907,0.996034,0.0004207822,0.003219644,0.0002242697,0.000003058141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03663201,0.000274245,0.9599643,0.0002112442,0.00002749848,0.00005389504,0.00008213891,0.0002648254,0.002489777],"genre_scores_gemma":[0.858241,0.0003723065,0.1384183,0.00009140458,0.00002843299,0.0001145941,0.0002020197,0.0001186288,0.00241333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003747725,"threshold_uncertainty_score":0.01005679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952795772413748,"score_gpt":0.2413940479953971,"score_spread":0.2218660902712596,"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."}}