{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009312497,0.0001113653,0.0001082403,0.0001181503,0.00005650627,0.0000197181,0.00007359643,0.00009610695,0.0001016177],"category_scores_gemma":[0.00005079166,0.0001073847,0.00004008199,0.0001675899,0.00001523426,0.0000305901,0.00001206282,0.0002554802,0.000166985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006004555,"about_ca_system_score_gemma":0.000005112122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000163374,"about_ca_topic_score_gemma":0.000003829894,"domain_scores_codex":[0.9994081,0.00001871412,0.0001276161,0.0001358609,0.0001276508,0.0001821183],"domain_scores_gemma":[0.9997168,0.00005760512,0.0000224912,0.000159922,0.00002221733,0.00002095344],"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":[0.000006245692,0.000004307361,0.01427619,0.00003273608,0.000006009258,0.000001524641,0.00006287361,0.9788469,0.00139712,0.000209006,0.00003822485,0.005118841],"study_design_scores_gemma":[0.0002262804,0.00003694893,0.001246792,0.00004139786,0.000003590178,8.344738e-7,0.0001093426,0.9808485,0.01438124,0.00001912777,0.002975025,0.0001109486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846765,0.00001512031,0.002992121,0.0000421119,0.0003681934,0.0001130771,0.000005867032,0.001736361,0.01005059],"genre_scores_gemma":[0.9994012,0.00001092585,0.0001348015,0.00003512137,0.00003292409,0.000003678138,0.00002181416,0.00002856013,0.0003310133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0147246,"threshold_uncertainty_score":0.4379021,"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."}}