{"id":"W2034754696","doi":"10.1371/journal.pcbi.1002894","title":"Starling Flock Networks Manage Uncertainty in Consensus at Low Cost","year":2013,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Army Research Office; Division of Electrical, Communications and Cyber Systems; Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Princeton University; Air Force Office of Scientific Research; City University of New York; National Science Foundation","keywords":"Flocking (texture); Flock; Robustness (evolution); Parameterized complexity; Starling; Computer science; Group cohesiveness; Econometrics; Mathematics; Biology; Algorithm; Ecology; Psychology; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00218721,0.0007233114,0.001401669,0.0009929413,0.0009787173,0.001681782,0.001613968,0.001382541,0.001496257],"category_scores_gemma":[0.01148379,0.0007525069,0.0006037143,0.0004612008,0.001521589,0.003636483,0.002148879,0.0009762518,0.0003250467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009584091,"about_ca_system_score_gemma":0.0006840468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001834249,"about_ca_topic_score_gemma":0.001422071,"domain_scores_codex":[0.9991715,0.0002782319,0.00005423589,0.0002051217,0.0001603948,0.0001305994],"domain_scores_gemma":[0.9928228,0.003835633,0.001637287,0.0007373006,0.0005409094,0.0004261162],"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":[0.0001848624,0.00004798549,0.004282812,0.0001298703,0.0001305222,0.0002367183,0.0003214297,0.9333982,0.009865665,0.03345017,0.0005464547,0.0174052],"study_design_scores_gemma":[0.00002085251,0.0001327958,0.001889619,0.00001354212,0.00003523735,0.00006676602,0.0001253016,0.9470229,0.001402167,0.0487811,0.000484258,0.00002558658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.570307,0.0003325726,0.4253146,0.0004785881,0.00003189399,0.00007577585,0.00009882799,0.0003049505,0.003055767],"genre_scores_gemma":[0.9818864,0.0001115645,0.01713695,0.00005072454,0.00001730927,0.00005576992,0.00007046012,0.00003154205,0.0006392956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00218721,"threshold_uncertainty_score":0.01156718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819828734327489,"score_gpt":0.2364378643344134,"score_spread":0.2182395769911385,"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."}}