{"id":"W2600809465","doi":"10.3837/tiis.2016.12.002","title":"Cluster-based Cooperative Data Forwarding with Multi-radio Multi-channel for Multi-flow Wireless Networks","year":2016,"lang":"en","type":"article","venue":"KSII Transactions on Internet and Information Systems","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research and Materiel Command; Khulna University; University of British Columbia; Chinese University of Hong Kong; Nanyang Technological University; Auckland University of Technology, New Zealand; City University of Hong Kong","keywords":"Computer science; Computer network; Channel (broadcasting); Cluster (spacecraft); Wireless; Flow (mathematics); Wireless network; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031696,0.0003801871,0.0004195921,0.0006501126,0.0008709577,0.0004557849,0.0009424368,0.000512775,0.0006257035],"category_scores_gemma":[0.001797578,0.0001872394,0.0004405183,0.0006878997,0.0005738878,0.000852422,0.0006405147,0.0003882987,0.00009265887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009685913,"about_ca_system_score_gemma":0.0009467901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005899505,"about_ca_topic_score_gemma":0.007259427,"domain_scores_codex":[0.9996562,0.0001066094,0.00001032581,0.00005331829,0.0001237286,0.00004977979],"domain_scores_gemma":[0.9992906,0.0003713161,0.00005996163,0.0001184534,0.0001316064,0.00002804086],"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":[0.0001571305,0.0001252925,0.001341706,0.0001287359,0.00007269341,0.0001659253,0.0002948823,0.8360884,0.01912163,0.0290798,0.001896314,0.1115276],"study_design_scores_gemma":[0.00001246021,0.00009368467,0.0001936152,0.000003711921,0.00001703377,0.00005586474,0.00002020275,0.9920502,0.00333252,0.003051924,0.00115726,0.00001153518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06864744,0.0004928189,0.9282829,0.0001409724,0.00004619543,0.00007552875,0.00002270931,0.0003272785,0.001964299],"genre_scores_gemma":[0.8937736,0.0002750161,0.1048646,0.00006175373,0.00002145396,0.00008462706,0.00003861473,0.00001554941,0.0008648798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005899505,"threshold_uncertainty_score":0.01173037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0714968182210633,"score_gpt":0.2850869904874533,"score_spread":0.21359017226639,"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."}}