{"id":"W2204949160","doi":"10.1109/lwc.2015.2483509","title":"Wireless Access Point Localization Using Nonlinear Least Squares and Multi-Level Quality Control","year":2015,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"RSS; Computer science; Wireless; A priori and a posteriori; Nonlinear system; Path loss; Path (computing); Non-linear least squares; Algorithm; Point (geometry); Radio propagation; Wireless network; Real-time computing; Mathematical optimization; Estimation theory; Computer network; 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":[0.001008726,0.0007296107,0.0005537671,0.0005099421,0.0003530707,0.0008717203,0.001137802,0.0006028796,0.0007184116],"category_scores_gemma":[0.004536629,0.0003553604,0.0005439148,0.0008233807,0.0008249355,0.001572223,0.001313298,0.0008894865,0.0003656606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007465809,"about_ca_system_score_gemma":0.0008392244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005838259,"about_ca_topic_score_gemma":0.004420554,"domain_scores_codex":[0.9984763,0.0003725307,0.00007882417,0.0003758994,0.0006165238,0.000079912],"domain_scores_gemma":[0.998531,0.0004786909,0.0003084748,0.0002142511,0.0004257325,0.00004187628],"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":[0.0002517958,0.0001429871,0.004277105,0.0002414928,0.0001133064,0.0001225095,0.0003063114,0.4614474,0.05556959,0.01397646,0.001030445,0.4625207],"study_design_scores_gemma":[0.00001291985,0.00005490677,0.0006332699,0.00000435103,0.000009017101,0.00003155853,0.00001110314,0.9922414,0.005231772,0.001202925,0.0005505125,0.00001618897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006172882,0.00004662634,0.9932233,0.00003360637,0.00001136868,0.0000123886,0.000008232024,0.0002179329,0.0002735857],"genre_scores_gemma":[0.5634945,0.0002124159,0.4340295,0.00007887306,0.00005678452,0.0001172582,0.0000961886,0.00009302864,0.001821465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005838259,"threshold_uncertainty_score":0.01160854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1337875556096937,"score_gpt":0.3343690577827704,"score_spread":0.2005815021730767,"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."}}