{"id":"W4293057691","doi":"10.1109/vtc2022-spring54318.2022.9860619","title":"Edge-assisted human-to-virtual twin connectivity scheme for human digital twin frameworks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scheme (mathematics); Enhanced Data Rates for GSM Evolution; Process (computing); Reliability (semiconductor); Markov decision process; Distributed computing; Markov process; Artificial intelligence; 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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005128378,0.0007596079,0.0008619439,0.00115494,0.00105859,0.0003969638,0.001598342,0.001236623,0.0006769028],"category_scores_gemma":[0.0001775705,0.000958606,0.0003499564,0.001690206,0.0003618662,0.0006507583,0.0005629031,0.003872108,0.0001483363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006643976,"about_ca_system_score_gemma":0.0001553528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002293466,"about_ca_topic_score_gemma":0.00004496153,"domain_scores_codex":[0.9958946,0.00005959093,0.001012138,0.001021523,0.0007765538,0.001235641],"domain_scores_gemma":[0.9978383,0.0001579113,0.00019528,0.001273671,0.0002523253,0.0002825345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001989543,0.001867841,0.01600938,0.000748103,0.002258108,0.0003989734,0.001587481,0.06058032,0.2192946,0.6222216,0.02116022,0.05367441],"study_design_scores_gemma":[0.01530538,0.005931363,0.01466575,0.000921477,0.0006167524,0.0005418892,0.01647452,0.07682262,0.112821,0.06688526,0.676859,0.012155],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241161,0.0001251915,0.06171235,0.0007220483,0.001689227,0.001580739,0.0005104134,0.003282271,0.006261698],"genre_scores_gemma":[0.9953131,0.000004261154,0.0009506742,0.0002202245,0.0001660022,0.002207879,0.000131749,0.0001960556,0.0008100293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6556988,"threshold_uncertainty_score":0.9992865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02228319185285374,"score_gpt":0.2583394857557931,"score_spread":0.2360562939029393,"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."}}