{"id":"W2910779736","doi":"10.3390/s19020324","title":"Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Extended Kalman filter; Wireless; Computer science; Wireless sensor network; Artificial intelligence; Kalman filter; Artificial neural network; Real-time computing; Wireless network; Machine learning; Data mining; Telecommunications; Computer network","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":[0.0009356153,0.0007685858,0.0006911301,0.0009468668,0.0002763309,0.000669577,0.0006490722,0.0005494389,0.0004934782],"category_scores_gemma":[0.00429183,0.0002339368,0.0004109328,0.0007015811,0.0003876893,0.001230246,0.0007133901,0.0007149677,0.0001848254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004849429,"about_ca_system_score_gemma":0.0004334243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003458243,"about_ca_topic_score_gemma":0.002271237,"domain_scores_codex":[0.9992805,0.0001433337,0.0000566888,0.0002022197,0.0002467402,0.00007051954],"domain_scores_gemma":[0.9982503,0.0008664606,0.0002905704,0.0001370215,0.0004183232,0.00003735556],"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":[0.0001297472,0.0000569289,0.007937625,0.00008249452,0.0000549314,0.0001153483,0.00006769164,0.7920223,0.00475571,0.001771842,0.0005230134,0.1924825],"study_design_scores_gemma":[0.000001942607,0.00001778266,0.0008113416,0.000005133441,0.000005815453,0.00002212031,0.000005522325,0.9971269,0.001395244,0.0005105809,0.00009181083,0.000005825594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05152239,0.0003447545,0.9465859,0.00008529632,0.00003124397,0.00002104899,0.00004989726,0.0004894894,0.0008700955],"genre_scores_gemma":[0.9535385,0.0002452903,0.04538177,0.00003918766,0.00003160387,0.00003521218,0.00008567946,0.00002127575,0.0006214377],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003458243,"threshold_uncertainty_score":0.00687623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004617575737699861,"score_gpt":0.1798695773789948,"score_spread":0.175252001641295,"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."}}