{"id":"W4391984700","doi":"10.1109/jiot.2024.3360414","title":"Digital-Twin-Based 3-D Map Management for Edge-Assisted Device Pose Tracking in Mobile AR","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Carleton University; University of Waterloo","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Computer vision; Artificial intelligence","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":[0.0002466462,0.0001502866,0.0001882827,0.0003256203,0.00002041169,0.0003490721,0.0001906458,0.00006980714,0.0000287961],"category_scores_gemma":[0.00001399412,0.0001442693,0.0001457285,0.0001523941,0.00001710368,0.0003339412,0.00001064458,0.0002392974,0.00001233243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001645282,"about_ca_system_score_gemma":0.00001639564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004189496,"about_ca_topic_score_gemma":0.000003040114,"domain_scores_codex":[0.9989592,0.00001282185,0.0004783564,0.0001401393,0.0001925728,0.0002168675],"domain_scores_gemma":[0.9996183,0.00008620127,0.00006192839,0.0001000916,0.00006957849,0.00006388478],"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":[0.00006485611,0.0001052644,0.0001859686,0.001531578,0.0002231808,0.0002212207,0.001035706,0.8842424,0.005437949,0.0002447971,0.007312048,0.09939508],"study_design_scores_gemma":[0.0005653558,0.0001122429,0.0001060348,0.001617095,0.00004592331,0.00004109069,0.0001379972,0.9672676,0.01460287,0.0003080886,0.01499082,0.0002048923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.232616,0.001485779,0.7591611,0.0001602532,0.003715484,0.0004118173,0.00001268995,0.000192461,0.002244505],"genre_scores_gemma":[0.9954528,0.00002803726,0.003822451,0.00008011455,0.000131716,0.00001166022,0.00001012564,0.00005135794,0.0004118051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7628368,"threshold_uncertainty_score":0.5883132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01527989529628834,"score_gpt":0.2533234859444427,"score_spread":0.2380435906481543,"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."}}