{"id":"W1992432313","doi":"10.1002/net.1022","title":"Efficient communication in unknown networks","year":2001,"lang":"en","type":"article","venue":"Networks","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Broadcasting (networking); Computer science; Dissemination; Node (physics); Synchronization (alternating current); Overhead (engineering); Computer network; Constant (computer programming); Simple (philosophy); Network topology; State (computer science); Limit (mathematics); Binary logarithm; Distributed computing; Broadcast communication network; Telecommunications network; Theoretical computer science; Algorithm; Topology (electrical circuits); Mathematics; Discrete mathematics; Telecommunications; Combinatorics; Channel (broadcasting)","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":[0.0002320937,0.0001160834,0.0001193386,0.00003995907,0.00006721951,0.00001635921,0.0001637693,0.0001952109,0.000002474232],"category_scores_gemma":[0.00001913562,0.0001060996,0.00006368107,0.0002477983,0.00006946144,0.000001044301,0.00009491516,0.0001586489,0.000001219791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001856786,"about_ca_system_score_gemma":0.000008733244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001342627,"about_ca_topic_score_gemma":0.0001996768,"domain_scores_codex":[0.9991962,0.00007726045,0.0001881708,0.0002355731,0.00006591268,0.0002368805],"domain_scores_gemma":[0.9993842,0.00001115275,0.00006519143,0.0004515009,0.00004561873,0.00004234303],"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":[0.0002219065,0.000289422,0.01636594,0.000003862486,0.00005460488,0.00001751362,0.00002587687,0.8830448,0.01432653,0.0003717004,0.004648223,0.08062957],"study_design_scores_gemma":[0.0007954992,0.0001953057,0.007181925,0.000101165,0.00003481361,0.00004631292,0.0000589341,0.9036847,0.006304292,0.000133206,0.0808563,0.0006075643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8477321,0.004235318,0.1415395,0.00036304,0.0001130408,0.0002354038,0.000001003798,0.00009208461,0.005688419],"genre_scores_gemma":[0.9957921,0.001496032,0.001788464,0.0003099008,0.0001839476,0.00000873613,0.0001027507,0.00001399787,0.0003040811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1480599,"threshold_uncertainty_score":0.4326616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007117616545489455,"score_gpt":0.2589590344058749,"score_spread":0.2518414178603854,"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."}}