{"id":"W2950674970","doi":"10.48550/arxiv.1303.2709","title":"Resilient Continuous-Time Consensus in Fractional Robust Networks","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Network topology; Robustness (evolution); Multi-agent system; Computer science; Consensus; Topology (electrical circuits); Metric (unit); Mathematics; Mathematical optimization; Artificial intelligence; Combinatorics; Computer network; Engineering","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":[0.0004736299,0.0004679612,0.0006603715,0.0003795739,0.0001269431,0.0002759318,0.002096989,0.0005628283,0.0001296146],"category_scores_gemma":[0.00008576176,0.0005616911,0.0002630022,0.0005987121,0.0001330985,0.0002965578,0.001591886,0.001026064,0.0007775406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006144958,"about_ca_system_score_gemma":0.0002764652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022659,"about_ca_topic_score_gemma":0.0000843807,"domain_scores_codex":[0.9966806,0.0004305612,0.0004907731,0.00153989,0.0001821503,0.0006760148],"domain_scores_gemma":[0.9969677,0.0003639137,0.0005365998,0.001554515,0.0003084777,0.0002687638],"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":[0.00003320851,0.0001432527,0.00461825,0.00002174161,0.00009903384,0.0005442579,0.0000392158,0.9578968,0.00002045712,0.03136257,0.005025301,0.000195924],"study_design_scores_gemma":[0.001163774,0.00002924602,0.00559839,0.0001496266,0.00003279264,0.00001264666,0.00003845755,0.9886863,0.000006200025,0.002582857,0.00115343,0.0005463304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1501008,0.0001575162,0.8415437,0.0003229881,0.001722157,0.0010418,0.00004875386,0.0004128627,0.00464943],"genre_scores_gemma":[0.9944574,0.00003267534,0.000821696,0.00009981728,0.0001513723,0.000006494778,0.00006262178,0.00002431301,0.004343621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8443567,"threshold_uncertainty_score":0.9996834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04425929013222003,"score_gpt":0.1735015944365517,"score_spread":0.1292423043043317,"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."}}