{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005006932,0.0001057381,0.00009456398,0.0001137745,0.00002385783,0.0001360087,0.00008429248,0.0001544294,0.000006274074],"category_scores_gemma":[0.0000150899,0.0001087468,0.00004107806,0.0005048778,0.000007505508,0.0009709444,0.000009153966,0.0001840324,0.0000359952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005938505,"about_ca_system_score_gemma":0.0000119807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.836663e-7,"about_ca_topic_score_gemma":0.000009802066,"domain_scores_codex":[0.9994283,0.000001634778,0.0001526529,0.00009588389,0.00007485013,0.0002466215],"domain_scores_gemma":[0.9997789,0.00007741899,0.000009608436,0.00006994759,0.00002311681,0.00004098074],"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":[0.000004238162,0.000006283163,0.000355512,0.00003081717,0.000007417934,2.735798e-7,0.00004832624,0.9458064,0.00004012145,0.001141394,0.02938172,0.02317747],"study_design_scores_gemma":[0.000244732,0.000006831008,0.0002402913,0.00001773622,0.000001566022,0.000001534457,0.00006162022,0.9962469,0.0004713056,0.0003407977,0.002233893,0.0001327558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1672679,0.0000159526,0.6160015,0.0002365237,0.0005843313,0.0006771421,0.0001093717,0.002618933,0.2124884],"genre_scores_gemma":[0.9962212,0.000008715753,0.0002114478,0.000025757,0.00008532076,0.00007833061,0.0001598813,0.00003346379,0.003175846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8289534,"threshold_uncertainty_score":0.4434564,"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."}}