{"id":"W3109081127","doi":"10.1109/ccece47787.2020.9255727","title":"Wireless Positioning Network Location Prediction Based on Machine Learning Techniques","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Wireless; Wireless network; Wireless sensor network; Artificial neural network; Node (physics); Key distribution in wireless sensor networks; Position (finance); Computer network; Artificial intelligence; Real-time computing; Wi-Fi array; Machine learning; 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":[0.000456128,0.00056852,0.0004478012,0.0009647167,0.0002871589,0.0004250377,0.0005536051,0.000478531,0.000957814],"category_scores_gemma":[0.002044753,0.0001675503,0.0003192764,0.000815683,0.0001861812,0.0007871242,0.0002955277,0.0005429392,0.0005398986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004424422,"about_ca_system_score_gemma":0.0003699372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005229911,"about_ca_topic_score_gemma":0.004731541,"domain_scores_codex":[0.9996137,0.00009029136,0.00002881117,0.0001006078,0.0001321766,0.00003446842],"domain_scores_gemma":[0.9992042,0.0003356035,0.0001098386,0.00006359758,0.0002702017,0.00001657718],"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":[0.0001322454,0.0001048835,0.009443537,0.00008634957,0.00006386508,0.0001063399,0.00005644126,0.6293951,0.005086093,0.001470297,0.001735887,0.3523189],"study_design_scores_gemma":[0.000002506852,0.00002661679,0.00107626,0.000006569597,0.000007059356,0.00002157949,0.000008836178,0.9966466,0.001523143,0.000379032,0.000297175,0.000004634109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08672225,0.0005938194,0.9078285,0.0002310612,0.0001145719,0.00005185392,0.0001352783,0.00134496,0.002977729],"genre_scores_gemma":[0.8907921,0.0004845071,0.1054481,0.00005872645,0.0000629542,0.00007358126,0.000262814,0.00003332957,0.002783969],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005229911,"threshold_uncertainty_score":0.01039898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006840212832025952,"score_gpt":0.1833826576362859,"score_spread":0.17654244480426,"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."}}