{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001402583,0.0002513047,0.0003633779,0.0002486699,0.0001348217,0.00004759738,0.0002459523,0.0001951569,0.00005328001],"category_scores_gemma":[0.0004459705,0.0002427944,0.0001829982,0.002038924,0.0001446724,0.0001609212,0.00006467693,0.0001618613,0.000002933229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007532143,"about_ca_system_score_gemma":0.000182555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001471552,"about_ca_topic_score_gemma":0.00009745815,"domain_scores_codex":[0.9985196,0.000065286,0.000405444,0.0003753375,0.0002273517,0.0004069973],"domain_scores_gemma":[0.9989786,0.00004028919,0.00006034718,0.0004251764,0.000381461,0.0001140982],"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":[0.00002554202,0.00003625622,0.01235476,0.0004088676,0.00008400956,0.00001256592,0.0002579787,0.983427,0.000207845,0.0009843615,0.00003653,0.002164235],"study_design_scores_gemma":[0.0002747988,0.00003817163,0.001684183,0.00000758977,0.0001847026,3.319772e-7,0.0006726863,0.9858765,0.009178526,0.001086698,0.0007136147,0.0002822132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680366,0.0000986301,0.830284,0.0003155522,0.00007276153,0.0001883271,0.00001709588,0.0009403608,0.0000467246],"genre_scores_gemma":[0.9987463,0.000009364275,0.0008260374,0.0002081023,0.00003344197,0.00001855574,0.0001040653,0.00004017571,0.00001390091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8307098,"threshold_uncertainty_score":0.9900867,"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."}}