{"id":"W4223997090","doi":"10.21203/rs.3.rs-1527636/v1","title":"Augmented Reality indoor tracking using Placenote","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Association of Universities and Colleges of Canada","funders":"","keywords":"Augmented reality; Computer science; Global Positioning System; Tracking (education); Real-time computing; Tracking system; Destinations; Computer vision; Artificial intelligence; Geography; Telecommunications; Tourism","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":[0.000281364,0.0005869623,0.000552806,0.0005512885,0.0003390447,0.001195902,0.000936282,0.0006459191,0.002212194],"category_scores_gemma":[0.0006832985,0.0002779218,0.0005696632,0.0009960833,0.000338531,0.001174705,0.0009309354,0.0004920642,0.000902874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005314882,"about_ca_system_score_gemma":0.0005457896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004902857,"about_ca_topic_score_gemma":0.002967066,"domain_scores_codex":[0.9996423,0.00006135905,0.00001852707,0.0001147266,0.000119643,0.00004349722],"domain_scores_gemma":[0.9996694,0.00005964751,0.00005507483,0.0001069805,0.00008522472,0.00002365727],"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":[0.0005767485,0.0001954049,0.005486013,0.0002004894,0.0000935592,0.0005550304,0.0002623545,0.5673074,0.03881326,0.01181556,0.003984958,0.3707092],"study_design_scores_gemma":[0.00001685555,0.0001402451,0.001013643,0.00001767527,0.00002848564,0.0002078787,0.00003551321,0.9794655,0.01287328,0.001084574,0.005086641,0.00002976502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03496925,0.0001979716,0.9583586,0.00008419628,0.00009685573,0.00003233567,0.0001155918,0.002747947,0.003397149],"genre_scores_gemma":[0.8958431,0.0003309745,0.09962789,0.00005371181,0.00003192716,0.0000344186,0.0002356613,0.00005998412,0.003782259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004902857,"threshold_uncertainty_score":0.009748638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1393334174904883,"score_gpt":0.4093233766460258,"score_spread":0.2699899591555375,"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."}}