{"id":"W2546957331","doi":"10.1109/acssc.2012.6489342","title":"Diffusion least-mean squares over distributed networks in the presence of MAC errors","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Convergence (economics); Steady state (chemistry); Node (physics); Diffusion; Computer science; Stability (learning theory); Monte Carlo method; Algorithm; Mean squared error; Least-squares function approximation; Standard deviation; Mathematical optimization; Mathematics; Statistics; Engineering; Machine learning","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":[0.0001313955,0.000107238,0.0001085498,0.00003705328,0.00001744815,0.000005851557,0.0002026374,0.00005067385,0.00007590713],"category_scores_gemma":[0.00002529958,0.00007475429,0.00003004837,0.0001694724,0.00003868014,0.0001776304,0.000065742,0.0001493283,0.000002639438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003075026,"about_ca_system_score_gemma":0.000001399825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004299045,"about_ca_topic_score_gemma":0.00003712121,"domain_scores_codex":[0.9993781,0.00002890266,0.0001536046,0.00007467853,0.0001182182,0.0002465157],"domain_scores_gemma":[0.9996014,0.00009103938,0.00002360492,0.0002416102,0.00001175516,0.00003053755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001142777,0.0005434601,0.2371456,0.0002908348,0.00006975247,0.00001688212,0.005469641,0.6030937,0.06987852,0.0364382,0.02108421,0.02585492],"study_design_scores_gemma":[0.0004024016,0.00008225236,0.5537861,0.0002109278,0.00001365742,0.000008359963,0.0006269818,0.4151919,0.01814696,0.001269143,0.009741426,0.0005199407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4313822,0.0002107899,0.5663349,0.00001874356,0.0001008995,0.0001863307,0.00001338351,0.0002877841,0.001464974],"genre_scores_gemma":[0.9969579,0.00002586592,0.00286534,0.00001405167,0.00004850506,0.00002032933,0.00001139349,0.00001623852,0.00004037399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5655757,"threshold_uncertainty_score":0.3048392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335440637493644,"score_gpt":0.24434151338916,"score_spread":0.2309871070142235,"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."}}