{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001664325,0.00057784,0.0005563709,0.001378091,0.0004144887,0.0007423692,0.0007299022,0.0004538943,0.002570757],"category_scores_gemma":[0.0006057151,0.000201788,0.0004556574,0.001512141,0.000214822,0.0007765712,0.0008417816,0.0003149534,0.001811455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001920377,"about_ca_system_score_gemma":0.0003179426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001692025,"about_ca_topic_score_gemma":0.001371736,"domain_scores_codex":[0.9994512,0.00009468623,0.00002698568,0.0001257326,0.0002241258,0.00007731059],"domain_scores_gemma":[0.9997699,0.00003766424,0.00003368896,0.0000471339,0.00009902322,0.00001257687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002906544,0.000072681,0.006938154,0.0006736471,0.0001118818,0.000602185,0.0002052815,0.01832968,0.05179877,0.007601505,0.01188053,0.901495],"study_design_scores_gemma":[0.0002164142,0.001531938,0.03109185,0.0005593612,0.0007458267,0.01218917,0.0006543533,0.5749373,0.1284732,0.01038489,0.238775,0.0004405047],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02985412,0.004351209,0.9372749,0.0002003849,0.00031142,0.00008619457,0.0002947602,0.006702362,0.0209248],"genre_scores_gemma":[0.7796309,0.005021767,0.1968702,0.0002401377,0.0002930227,0.0002050714,0.001013056,0.0001709711,0.0165548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002570757,"threshold_uncertainty_score":0.008599997,"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."}}