{"id":"W3083085256","doi":"10.1109/lwc.2020.3021991","title":"RSS Localization Under Gaussian Distributed Path Loss Exponent Model","year":2020,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"RSS; Estimator; Mathematics; Maximum a posteriori estimation; Algorithm; Gaussian; Cramér–Rao bound; Statistics; Random variable; Node (physics); Computer science; Mathematical optimization; Maximum likelihood","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":[0.00114002,0.001140928,0.0009854091,0.0006670093,0.0002554858,0.000903498,0.001710416,0.001619893,0.0009563116],"category_scores_gemma":[0.00301011,0.0004012329,0.0007356647,0.001586892,0.001274746,0.001769427,0.001090318,0.0007939901,0.0008211702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006403344,"about_ca_system_score_gemma":0.0006296109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005891019,"about_ca_topic_score_gemma":0.00327697,"domain_scores_codex":[0.9990439,0.0002818262,0.00002984709,0.0002577058,0.0002340471,0.0001526003],"domain_scores_gemma":[0.9990768,0.0004153574,0.0001601704,0.0001200204,0.0002000522,0.00002753061],"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":[0.00009035238,0.00002440304,0.001497492,0.000103871,0.00003658886,0.0005124911,0.00008413515,0.9574966,0.003900199,0.0242737,0.0009764782,0.01100369],"study_design_scores_gemma":[0.000007747065,0.00003406768,0.000293176,0.000003887542,0.00001035535,0.00008933998,0.00001679821,0.9939015,0.0004459246,0.004823826,0.0003606517,0.0000126802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02712897,0.0006285651,0.9691681,0.0002593089,0.00004660489,0.00002879877,0.0001849151,0.000370495,0.002184286],"genre_scores_gemma":[0.9225475,0.002696266,0.06483143,0.0001578501,0.0001563692,0.0001009518,0.0004318143,0.0001023579,0.008975481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005891019,"threshold_uncertainty_score":0.01171345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02766037570336851,"score_gpt":0.2304191522606775,"score_spread":0.202758776557309,"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."}}