{"id":"W3116869432","doi":"10.3390/su122410627","title":"Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Linear discriminant analysis; Naive Bayes classifier; Bluetooth; Decision tree; Support vector machine; Bluetooth Low Energy; Artificial intelligence; Trilateration; Real-time computing; Machine learning; Wireless; Recursive Bayesian estimation; Data mining; Bayesian probability; Telecommunications; Engineering","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":[0.0002860498,0.000385356,0.0004718002,0.000806424,0.0002970984,0.0003836193,0.0004980523,0.0003453534,0.001254255],"category_scores_gemma":[0.0006771804,0.0001703468,0.0002750118,0.0005930742,0.0001694694,0.0006574926,0.0003957127,0.0002274303,0.001058102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002344101,"about_ca_system_score_gemma":0.0001782302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001747703,"about_ca_topic_score_gemma":0.002281102,"domain_scores_codex":[0.9997076,0.00006281171,0.00001301683,0.00008870966,0.0001012036,0.0000266733],"domain_scores_gemma":[0.9997656,0.00005737557,0.00003217624,0.00003409357,0.00009852919,0.00001220444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004904096,0.0002501674,0.007208471,0.0001885025,0.00008810005,0.0002989786,0.0002029476,0.05868497,0.1011961,0.002924169,0.004428557,0.8240385],"study_design_scores_gemma":[0.00003508531,0.0002940319,0.004996065,0.0000285395,0.00005617813,0.0004988119,0.00006549487,0.9505886,0.03644336,0.001445261,0.005486007,0.00006261662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07200573,0.0003355415,0.9195733,0.0001479755,0.00009003853,0.00003802958,0.0001094874,0.003606274,0.004093629],"genre_scores_gemma":[0.8617685,0.0002177783,0.1327979,0.00008879425,0.00003342929,0.00005301899,0.0001912803,0.00007013694,0.004779288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001747703,"threshold_uncertainty_score":0.004195929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009116049326240515,"score_gpt":0.2401066105293075,"score_spread":0.230990561203067,"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."}}