{"id":"W1977335817","doi":"10.1109/ssp.2007.4301337","title":"Distributed Average Consensus using Probabilistic Quantization","year":2007,"lang":"en","type":"article","venue":"2007 IEEE/SP 14th Workshop on Statistical Signal Processing","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Quantization (signal processing); Probabilistic logic; Computer science; Computation; Distributed algorithm; Wireless sensor network; Consensus; Algorithm; Consensus algorithm; Theoretical computer science; Distributed computing; Artificial intelligence; Multi-agent system","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001656991,0.0005800018,0.0006421842,0.0002542372,0.0006385376,0.0008224626,0.0008382386,0.0003061293,0.00008266234],"category_scores_gemma":[0.000806565,0.0005539792,0.0001097895,0.001401606,0.0002754499,0.0004124622,0.0001172336,0.0006251187,0.0001459407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005475502,"about_ca_system_score_gemma":0.000341882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003623876,"about_ca_topic_score_gemma":0.00001171345,"domain_scores_codex":[0.9945839,0.0002801954,0.001263933,0.001188431,0.001293711,0.001389824],"domain_scores_gemma":[0.9958745,0.001849464,0.0005265884,0.000582512,0.0005665663,0.0006003327],"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":[0.001338011,0.002820406,0.001442738,0.001083139,0.0002958498,0.003153496,0.0008937838,0.287228,0.0117555,0.1689837,0.009500716,0.5115046],"study_design_scores_gemma":[0.001332016,0.0001230152,0.001008782,0.0005588263,0.00005711539,0.00008322443,0.0000790307,0.9918229,0.0005336429,0.002571114,0.00105435,0.0007759774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005672008,0.0001859721,0.9909179,0.0001854242,0.0007944542,0.0007213262,0.0002047849,0.0005161017,0.0008020794],"genre_scores_gemma":[0.9475016,0.000002267958,0.0515213,0.0002839464,0.0003960804,0.00001685501,0.0001371842,0.00005438856,0.00008635765],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9418296,"threshold_uncertainty_score":0.9996912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04012101147101355,"score_gpt":0.3098272831243882,"score_spread":0.2697062716533747,"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."}}