{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002267318,0.0007024442,0.001168963,0.001009613,0.00140267,0.002529453,0.001857109,0.001976486,0.004069179],"category_scores_gemma":[0.01224565,0.0005922923,0.0004558944,0.001562683,0.002329009,0.006346802,0.002969961,0.001369026,0.001000571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001115656,"about_ca_system_score_gemma":0.0008002159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009815225,"about_ca_topic_score_gemma":0.000787014,"domain_scores_codex":[0.997436,0.001140484,0.0001121093,0.0004714441,0.0005189665,0.0003210239],"domain_scores_gemma":[0.9908928,0.006671513,0.000575601,0.001287506,0.0004143965,0.0001581652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000409015,0.00008381194,0.0005449055,0.0005541993,0.00009141386,0.0002241282,0.0005150523,0.5794903,0.006944896,0.2594748,0.009061082,0.1426063],"study_design_scores_gemma":[0.00007363261,0.00007251725,0.0001979911,0.00004642928,0.00002460313,0.0001330078,0.0001134476,0.7473813,0.003593889,0.2347867,0.01355555,0.00002103763],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03768561,0.002091844,0.9479945,0.001576039,0.000118144,0.00008483822,0.0001098491,0.0006655121,0.009673627],"genre_scores_gemma":[0.7275169,0.002871057,0.2573178,0.000380509,0.0003052075,0.0003220521,0.0004131309,0.0001935624,0.01067988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004069179,"threshold_uncertainty_score":0.01361275,"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."}}