{"id":"W2126785684","doi":"10.1109/eit.2009.5189622","title":"Differential access points for indoor location estimation","year":2009,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Real-time computing; Estimation; Differential (mechanical device); Path (computing); Calibration; Global Positioning System; Limit (mathematics); Telecommunications; Engineering; Statistics; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002293108,0.00007753896,0.00007431417,0.00008771489,0.00004225791,0.00006116842,0.0001253444,0.00007669393,0.00006580175],"category_scores_gemma":[0.00004627252,0.00007065773,0.00002310083,0.0001477073,0.000009099662,0.0002235229,0.000007951853,0.00003807422,0.00001754276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000306814,"about_ca_system_score_gemma":0.000005040965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001612822,"about_ca_topic_score_gemma":0.000002456615,"domain_scores_codex":[0.9996066,0.000002548869,0.0001272591,0.00008030634,0.0000629831,0.0001203167],"domain_scores_gemma":[0.9997973,0.00001488327,0.00001445423,0.0001062156,0.00005028928,0.00001687155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004775642,0.0001016165,0.0008956339,0.0002247225,0.00004614885,8.554164e-7,0.0002451989,0.1230725,0.006430699,0.1213451,0.01817624,0.7294135],"study_design_scores_gemma":[0.0004524867,0.00004177791,0.007020214,0.00001313728,0.00001048953,9.91669e-7,0.00002045285,0.7830889,0.1935862,0.01514853,0.0004563877,0.0001603643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04367507,0.00001404376,0.9529998,0.000245349,0.0001775142,0.0002166947,0.00000224459,0.001072985,0.0015963],"genre_scores_gemma":[0.9951181,0.00000678371,0.004598272,0.00009129695,0.0000363325,0.00002273035,0.00004188041,0.000009221702,0.00007535209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9514431,"threshold_uncertainty_score":0.2881339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356156717861863,"score_gpt":0.2636994856909955,"score_spread":0.2501379185123769,"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."}}