{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002180965,0.0002029123,0.0003191188,0.000156255,0.0001138091,0.00009385576,0.0006375032,0.0001384671,0.00006016529],"category_scores_gemma":[0.00008931195,0.0001933418,0.00006400207,0.0003432811,0.00009682848,0.0001169793,0.0003172503,0.0001822642,0.000496693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000251371,"about_ca_system_score_gemma":0.00005599905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002567343,"about_ca_topic_score_gemma":0.00007204289,"domain_scores_codex":[0.9979862,0.0002890576,0.0004932717,0.0005470122,0.000180562,0.0005038806],"domain_scores_gemma":[0.9982964,0.0008921031,0.0001732902,0.0003086645,0.0002070898,0.0001224844],"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":[0.00001461071,0.0001037374,0.009871026,0.00001410959,0.00006331968,0.00002764961,0.0001055043,0.9658787,0.000352983,0.01751543,0.001153138,0.004899806],"study_design_scores_gemma":[0.0009341569,0.00003376275,0.01389602,0.0000275355,0.00000351534,0.00001444637,0.00001692503,0.9802793,0.0000134425,0.004098999,0.0004757381,0.0002061523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3934476,0.0001764862,0.6020178,0.001887104,0.0006480204,0.00109114,0.00004262419,0.0002305621,0.0004586111],"genre_scores_gemma":[0.993264,0.000002932246,0.005532617,0.0006570228,0.00009169092,0.0001317769,0.0002318419,0.00001034486,0.00007772641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5998164,"threshold_uncertainty_score":0.788425,"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."}}