{"id":"W1591044263","doi":"10.48550/arxiv.1310.7368","title":"Formulation and Steady-state Analysis of LMS Adaptive Networks for Distributed Estimation in the Presence of Transmission Errors","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Transmission (telecommunications); Estimation; State (computer science); Computer science; Steady state (chemistry); Control theory (sociology); Mathematics; Algorithm; Telecommunications; Engineering; Artificial intelligence; Control (management)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001522675,0.0001610331,0.0003152129,0.0002760927,0.0000237702,0.000007199093,0.0002235982,0.0001310938,0.000002509394],"category_scores_gemma":[0.00001901804,0.0001521373,0.0001053611,0.0005396784,0.00005594585,0.0001839757,0.00007296407,0.0001872259,6.001284e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006028003,"about_ca_system_score_gemma":0.00000815939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008617729,"about_ca_topic_score_gemma":0.00003533021,"domain_scores_codex":[0.9993079,0.00004718325,0.0002268948,0.0002395736,0.00004870369,0.0001297568],"domain_scores_gemma":[0.9992614,0.0002048368,0.0001766671,0.0002360152,0.00009441873,0.00002667679],"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":[0.00005224117,0.00001452012,0.0005464101,0.0001108335,0.0001552541,9.821163e-7,0.0003846276,0.994949,0.0001947429,0.002405537,0.000004774692,0.001181097],"study_design_scores_gemma":[0.000160942,0.00004128233,0.01386748,0.0001418533,0.0002724047,1.018998e-7,0.00008926004,0.9752141,0.0005020371,0.009574272,0.000005433765,0.0001308542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3735833,0.00002917171,0.6258233,0.000001883826,0.00001111788,0.0004081267,0.00008435561,0.00004242952,0.00001631306],"genre_scores_gemma":[0.9912535,0.0001434731,0.008433149,8.995436e-7,0.00000285671,0.000006596375,0.0001376478,0.00001246217,0.000009438647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6176701,"threshold_uncertainty_score":0.620398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04688997639601662,"score_gpt":0.2080682674346059,"score_spread":0.1611782910385893,"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."}}