{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002502214,0.0006461836,0.001249409,0.0008506504,0.000654935,0.001194042,0.001810116,0.0008588401,0.001295648],"category_scores_gemma":[0.009657795,0.0004082701,0.0006797305,0.001093013,0.001514808,0.002658799,0.001762202,0.001394106,0.0003227757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111846,"about_ca_system_score_gemma":0.00128894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002186405,"about_ca_topic_score_gemma":0.001258779,"domain_scores_codex":[0.9977334,0.0006045653,0.000136436,0.0004827488,0.0009166744,0.0001261799],"domain_scores_gemma":[0.9959681,0.002310695,0.0003623259,0.0005261936,0.0007335655,0.0000990905],"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":[0.000049019,0.00002275069,0.0002849006,0.00005884148,0.00003677089,0.00003871093,0.0001018994,0.8784413,0.002021628,0.07770679,0.0008243078,0.04041302],"study_design_scores_gemma":[0.000009782721,0.00002020785,0.00003319155,0.000003408484,0.000004201097,0.00001177749,0.000005400162,0.9761592,0.000539687,0.02273504,0.0004711658,0.000006935519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003167195,0.0001016258,0.9956495,0.00008058348,0.00002519279,0.00001849078,0.0000115542,0.0001495635,0.0007963273],"genre_scores_gemma":[0.6472687,0.0004390584,0.3492176,0.0001812013,0.0001399326,0.0002592229,0.000167424,0.0001167912,0.002210052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002502214,"threshold_uncertainty_score":0.01323313,"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."}}