{"id":"W4388584678","doi":"10.48550/arxiv.2311.04997","title":"Digital Twin-based 3D Map Management for Edge-assisted Device Pose Tracking in Mobile AR","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Carleton University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Upload; Computer vision; Benchmark (surveying); Artificial intelligence; Mobile device; Enhanced Data Rates for GSM Evolution; Real-time computing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002751837,0.0003249118,0.0003113825,0.0005448677,0.0001594182,0.0002750759,0.002123688,0.0002272531,0.000006494585],"category_scores_gemma":[0.00001659298,0.0004165378,0.0002338147,0.001027289,0.00008012431,0.0003604236,0.001548513,0.0003722936,0.0001615689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005441213,"about_ca_system_score_gemma":0.0001499527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000545158,"about_ca_topic_score_gemma":0.0001415441,"domain_scores_codex":[0.9976539,0.00006244716,0.0003234257,0.001374234,0.0001319341,0.0004540481],"domain_scores_gemma":[0.9977258,0.0002373151,0.0002694008,0.001488963,0.0001375884,0.0001409476],"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":[0.00005334042,0.000733302,0.001223573,0.0007424108,0.000260305,0.0004349172,0.0002517817,0.9237283,0.00002734455,0.05300854,0.00116864,0.01836755],"study_design_scores_gemma":[0.001478256,0.0000651812,0.005312484,0.0003795151,0.00009942814,0.00000158477,0.0002855041,0.9635972,0.0001128909,0.01453376,0.01331476,0.0008193976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0179184,0.00003054908,0.9766193,0.0002814146,0.0004382948,0.001856716,0.0001239618,0.0005692745,0.002162099],"genre_scores_gemma":[0.9906997,0.00002831276,0.005533074,0.0001279784,0.00004933709,0.0000794824,0.0002152846,0.00004135622,0.003225451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9727813,"threshold_uncertainty_score":0.9998286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09716954205279334,"score_gpt":0.2333684222472873,"score_spread":0.136198880194494,"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."}}