{"id":"W2730709293","doi":"10.1007/s12652-017-0531-3","title":"An experimental comparative study of RSSI-based positioning algorithms for passive RFID localization in smart environments","year":2017,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Radio-frequency identification; Kalman filter; Identification (biology); Wireless; Wireless sensor network; Computational intelligence; Field (mathematics); Particle filter; Real-time computing; Ambient intelligence; Received signal strength indication; Tracking (education); Smart environment; Filter (signal processing); Algorithm; Embedded system; Telecommunications; Artificial intelligence; Computer vision; Computer network; Internet of Things; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001703623,0.0007740565,0.0006472132,0.001138275,0.0003918359,0.0007828186,0.001109085,0.0008972893,0.003738218],"category_scores_gemma":[0.00742127,0.0003398258,0.0003514054,0.0009952106,0.0006127815,0.001758987,0.0008958593,0.000364462,0.000795822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003949307,"about_ca_system_score_gemma":0.0003351117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001524905,"about_ca_topic_score_gemma":0.001500343,"domain_scores_codex":[0.9982083,0.0007177354,0.0001542931,0.0002836518,0.000488284,0.0001477713],"domain_scores_gemma":[0.9938635,0.003282198,0.0003462554,0.0006841874,0.001697342,0.000126602],"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":[0.01718469,0.004435557,0.02519752,0.002987093,0.0006140358,0.0006229724,0.002320007,0.06856954,0.3658879,0.004474225,0.002732119,0.5049744],"study_design_scores_gemma":[0.001234367,0.03777311,0.09244701,0.0002261363,0.001304448,0.002668004,0.004592306,0.4532957,0.392335,0.002456506,0.01128534,0.0003820854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9186087,0.0004149559,0.07578716,0.00009689023,0.0001789344,0.0001614792,0.0004198177,0.0005922361,0.003739775],"genre_scores_gemma":[0.9724248,0.0002549183,0.02466763,0.00003252595,0.00002382363,0.00008549334,0.0004150367,0.0000713293,0.002024427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003738218,"threshold_uncertainty_score":0.01250559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04940855994119121,"score_gpt":0.3265348984183771,"score_spread":0.2771263384771859,"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."}}