{"id":"W2911526837","doi":"10.5430/air.v7n2p87","title":"Indoor Localization Based on Bluetooth","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Qinglan Project of Jiangsu Province of China; National Social Science Fund of China; Six Talent Peaks Project in Jiangsu Province; Government of Jiangsu Province","keywords":"Bluetooth; Hybrid positioning system; Fingerprint (computing); Global Positioning System; Computer science; Positioning technology; Real-time computing; Process (computing); Outlier; Terminal (telecommunication); Transmission (telecommunications); Indoor positioning system; Positioning system; Wireless; Computer vision; Artificial intelligence; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006830128,0.0001285678,0.0001323982,0.0005082039,0.0001291579,0.0001050247,0.0003463137,0.000165779,0.0008505072],"category_scores_gemma":[0.0002588535,0.0001218367,0.00004519309,0.00119034,0.0001342133,0.0001102098,0.00004013296,0.0004216969,0.004575252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001333186,"about_ca_system_score_gemma":0.00004651617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002584742,"about_ca_topic_score_gemma":0.00002186156,"domain_scores_codex":[0.9983263,0.00007457087,0.0002669285,0.0002515773,0.0005920736,0.0004885569],"domain_scores_gemma":[0.999035,0.0002497899,0.00001462129,0.0004279605,0.0002080246,0.000064595],"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":[0.00005100036,0.00008034008,0.001455927,0.00008177297,0.00001048353,0.000007831003,0.0002197245,0.7813691,0.004688808,0.09904335,0.001256411,0.1117353],"study_design_scores_gemma":[0.00001964881,0.000109061,0.00003525791,0.0000312454,9.963496e-7,3.005878e-7,0.0003506731,0.6922686,0.2941443,0.01035789,0.002564661,0.0001174023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1437499,0.0001186215,0.7842472,0.0005831051,0.001047014,0.001151866,0.00001218721,0.001757808,0.06733232],"genre_scores_gemma":[0.9993887,0.00003317161,0.0001939913,0.00007794075,0.00006579405,0.00003323512,0.00001266735,0.00003550026,0.0001590132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8556388,"threshold_uncertainty_score":0.9961998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08013421044768386,"score_gpt":0.3464721814307017,"score_spread":0.2663379709830178,"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."}}