{"id":"W4244365807","doi":"10.1109/glocom.2014.7417730","title":"Automatic Device-Transparent RSS-Based Indoor Localization","year":2014,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"RSS; Computer science; Set (abstract data type); k-nearest neighbors algorithm; Signal strength; Point (geometry); Transformation (genetics); Data mining; Artificial intelligence; Algorithm; Pattern recognition (psychology); Wireless; Mathematics; Telecommunications","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":[0.0005046918,0.0007218028,0.0007782613,0.001030993,0.0003724328,0.0008275365,0.001609023,0.0006407754,0.00169101],"category_scores_gemma":[0.00168497,0.0003609789,0.000368659,0.001147862,0.0003497769,0.001091999,0.001329512,0.0005776638,0.002891542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002225194,"about_ca_system_score_gemma":0.0002970681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007358547,"about_ca_topic_score_gemma":0.001064434,"domain_scores_codex":[0.9989059,0.0002049573,0.00005012673,0.0002203505,0.0005427906,0.00007591233],"domain_scores_gemma":[0.9988778,0.0001773073,0.0001810739,0.0004490997,0.0002860603,0.00002861406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006305676,0.0001575185,0.007024227,0.0003649974,0.0001296394,0.0004600539,0.0003851257,0.03356646,0.1765546,0.003772794,0.006906673,0.7700474],"study_design_scores_gemma":[0.0001091582,0.000609783,0.01677842,0.00007249136,0.0001452867,0.002778666,0.0001834742,0.6976302,0.2498431,0.004947254,0.0267052,0.0001969916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03355638,0.0002861235,0.9537447,0.00008042769,0.00006868319,0.00005791835,0.0001642472,0.009236352,0.002805112],"genre_scores_gemma":[0.7855983,0.0002203899,0.2081967,0.0001401757,0.00009552284,0.00009882801,0.0005667853,0.0002633012,0.004819994],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00169101,"threshold_uncertainty_score":0.005657017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04737749444252042,"score_gpt":0.3004627237144626,"score_spread":0.2530852292719422,"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."}}