{"id":"W2782795431","doi":"10.3390/s18010205","title":"Plils: A Practical Indoor Localization System through Less Expensive Wireless Chips via Subregion Clustering","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Wireless; Computer science; Exploit; Cluster analysis; Power (physics); Support vector machine; Artificial neural network; Phase (matter); Measure (data warehouse); Signal strength; Wireless network; Real-time computing; SIGNAL (programming language); Pattern recognition (psychology); Artificial intelligence; Electronic engineering; Data mining; Engineering; 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":[0.000251221,0.0005992696,0.0005754319,0.0007415041,0.0003331422,0.0004622183,0.001613514,0.0004612824,0.003050462],"category_scores_gemma":[0.0005602343,0.0002218062,0.0002794735,0.0007980273,0.0002922481,0.001077204,0.001116003,0.0003964412,0.002899226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000403399,"about_ca_system_score_gemma":0.0005938284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00191385,"about_ca_topic_score_gemma":0.003170771,"domain_scores_codex":[0.999667,0.00005170016,0.00001575491,0.0000895098,0.0001354808,0.00004050761],"domain_scores_gemma":[0.999579,0.00004832025,0.00006479285,0.000122334,0.0001527834,0.00003280668],"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":[0.0005703269,0.0001382885,0.003968159,0.0003506934,0.00007666577,0.0003143923,0.0003100173,0.06271182,0.1150917,0.005881451,0.01353295,0.7970535],"study_design_scores_gemma":[0.0001466936,0.0008050473,0.006679664,0.00004706651,0.0001054058,0.001197346,0.0003277468,0.7589493,0.1573545,0.004896881,0.0693272,0.0001631338],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02628544,0.0001674772,0.9616179,0.0001099039,0.00006014179,0.00007770459,0.0002013379,0.008031943,0.003448175],"genre_scores_gemma":[0.4087902,0.0001914426,0.5791068,0.0002153572,0.0000440399,0.000205948,0.0006097901,0.0002263138,0.01061001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003050462,"threshold_uncertainty_score":0.01020485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02603130270641757,"score_gpt":0.2536941574232174,"score_spread":0.2276628547167998,"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."}}