{"id":"W3048846505","doi":"10.3233/jifs-189134","title":"Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach","year":2020,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sheridan College","funders":"","keywords":"Computer science; Time division multiple access; Computer network; Scheduling (production processes); Schedule; Distributed computing; Markov chain; Broadcasting (networking); Wireless ad hoc network; Mathematical optimization; Wireless; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006473964,0.0005180121,0.0007896387,0.0007047679,0.0004510356,0.0005808474,0.001036312,0.0007270205,0.001729972],"category_scores_gemma":[0.0009772573,0.0002933709,0.0006397091,0.0004589706,0.0004134651,0.0005248562,0.0005084823,0.0005774301,0.0002097273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008875855,"about_ca_system_score_gemma":0.0009731723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006169121,"about_ca_topic_score_gemma":0.003926759,"domain_scores_codex":[0.9997495,0.00008198265,0.000009382577,0.00003647003,0.00008089285,0.00004171885],"domain_scores_gemma":[0.9997515,0.0001352039,0.00003266429,0.000008803212,0.00005297374,0.00001893105],"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":[0.00005437929,0.00004657805,0.0003576982,0.00006713499,0.00003263136,0.00003727161,0.00006401467,0.940526,0.00310449,0.01205491,0.0007946861,0.04286022],"study_design_scores_gemma":[0.000009383949,0.00002182709,0.00003798528,0.000003509734,0.000005212852,0.000008826524,0.000009296372,0.998334,0.0001969162,0.0009455722,0.0004244353,0.000003067141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0110149,0.0002800928,0.985163,0.0001513165,0.00006007518,0.00005045313,0.00001797283,0.0001047034,0.003157465],"genre_scores_gemma":[0.6026994,0.0006085816,0.3904964,0.0001602672,0.0001049743,0.0002801456,0.00008597266,0.00007667253,0.005487687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006169121,"threshold_uncertainty_score":0.0122664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03710510620566813,"score_gpt":0.2765061041024642,"score_spread":0.2394009978967961,"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."}}