{"id":"W2040937658","doi":"10.4028/www.scientific.net/amr.756-759.3946","title":"Simple and Robust RSSI Estimation Using M-Estimator","year":2013,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Estimator; Computer science; Simple (philosophy); Non-line-of-sight propagation; Kalman filter; Fading; Signal strength; Algorithm; Smoothing; Wireless; Real-time computing; Statistics; Mathematics; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002132809,0.0001019041,0.0001397818,0.0001629386,0.0001637027,0.0001659321,0.0001152868,0.00008893263,0.0003094735],"category_scores_gemma":[0.0002011797,0.00009549118,0.00000862394,0.0002119372,0.0001012596,0.0004212744,0.00008280517,0.00009872204,0.0001129146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000625144,"about_ca_system_score_gemma":0.00001179598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006137392,"about_ca_topic_score_gemma":0.000002177913,"domain_scores_codex":[0.9990982,0.00003539811,0.0001828313,0.0001495716,0.0001855583,0.0003484105],"domain_scores_gemma":[0.9995515,0.00006497074,0.0000158267,0.0001925693,0.0001231633,0.00005197209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004345047,0.000005085379,0.00004992411,0.0001341637,0.000005747468,0.000002087674,0.00003617462,0.09793282,0.8917099,0.00134101,0.0003823606,0.008396333],"study_design_scores_gemma":[0.0002183736,0.00002533459,0.0004200065,0.00003514824,0.000002291961,0.00000627562,0.0001311287,0.2407427,0.7473348,0.01063683,0.0002985006,0.0001486094],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9283545,0.0001441078,0.07002079,0.00004578877,0.0001433382,0.0003896755,0.00001082658,0.0004984107,0.00039256],"genre_scores_gemma":[0.9722075,0.00009359799,0.02750131,0.000007782307,0.00002837856,0.00007747981,0.00001581389,0.00003246753,0.00003570277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1443751,"threshold_uncertainty_score":0.3894017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04678990080761825,"score_gpt":0.3286030618464191,"score_spread":0.2818131610388008,"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."}}