{"id":"W3119750220","doi":"10.5539/nct.v5n2p34","title":"Indoor Localization Based on Optimized KNN","year":2020,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Fingerprint (computing); Hybrid positioning system; Hotspot (geology); Real-time computing; Positioning technology; Indoor positioning system; Wireless; Signal strength; Received signal strength indication; RSS; Positioning system; Artificial intelligence; Accelerometer; Telecommunications; Engineering; Node (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008930996,0.000167928,0.0001907269,0.00008469424,0.0001975891,0.00005615709,0.0004592705,0.0002549204,0.00001762272],"category_scores_gemma":[0.0001307437,0.0001590789,0.00002777186,0.0005714978,0.0001774728,0.00008719353,0.0001324369,0.0002977966,0.00001967564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002533029,"about_ca_system_score_gemma":0.000008025228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001408014,"about_ca_topic_score_gemma":0.000001456454,"domain_scores_codex":[0.9992676,0.0000331793,0.0002244292,0.0001635957,0.0001105421,0.000200702],"domain_scores_gemma":[0.9992346,0.00009867438,0.00004843849,0.0005482149,0.00004187417,0.0000282171],"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":[0.00004428168,0.00001934578,0.001226832,0.0000556839,0.00002432392,0.000001241167,0.0001283898,0.8281755,0.00006089259,0.02495782,0.01560194,0.1297037],"study_design_scores_gemma":[0.000514804,0.00009159603,0.00008907793,0.00004942198,0.00001146485,6.442484e-7,0.0003852635,0.9676048,0.00408728,0.004536921,0.02240445,0.0002243428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003609624,0.005900018,0.9413367,0.01216152,0.0001381682,0.0006728769,0.000009831771,0.02341849,0.0127528],"genre_scores_gemma":[0.9831453,0.002850478,0.01313943,0.0007068459,0.00001752321,0.00006935291,0.00003724981,0.00002695296,0.000006840659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9795357,"threshold_uncertainty_score":0.6487049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157829333716283,"score_gpt":0.19910371742433,"score_spread":0.1875254240871672,"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."}}