{"id":"W2565221469","doi":"10.1109/imis.2016.131","title":"AVN-AHH-VBF: Avoiding Void Node with Adaptive Hop-by-Hop Vector Based Forwarding for Underwater Wireless Sensor Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Forwarder; Computer network; Hop (telecommunications); Network packet; Wireless sensor network; Computer science; Flooding (psychology); Routing protocol; Real-time computing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003077225,0.000333658,0.0003643462,0.0000866064,0.0002394506,0.0001134533,0.0003249319,0.0001557401,0.00005057761],"category_scores_gemma":[0.000002804384,0.0002115612,0.000129539,0.0001474872,0.00004955043,0.0002579868,0.00005167455,0.0001773898,0.00004184386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964923,"about_ca_system_score_gemma":0.00002071294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004641117,"about_ca_topic_score_gemma":0.00006859022,"domain_scores_codex":[0.9984211,0.00008945591,0.0003715888,0.0003312776,0.0001996259,0.0005869811],"domain_scores_gemma":[0.9987125,0.000413023,0.00007465349,0.0005312981,0.000118658,0.000149824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007300686,0.0001725413,0.003019583,0.0004210617,0.001242727,0.00001044763,0.001105484,0.1044584,0.8548577,0.00132903,0.01135638,0.02129656],"study_design_scores_gemma":[0.003639375,0.0002219653,0.0000744259,0.0005413945,0.00008262433,0.00001363261,0.001105299,0.6504458,0.3101903,0.00007949465,0.03251503,0.001090627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07352665,0.0001097444,0.924056,0.0004796204,0.00009625537,0.0004921964,0.00003286872,0.0005234745,0.0006831727],"genre_scores_gemma":[0.9882404,0.00002576112,0.009864683,0.0001756306,0.0001567164,0.0001928802,0.00001992429,0.0001329742,0.001191085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9147137,"threshold_uncertainty_score":0.8627213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01637492692189464,"score_gpt":0.2027510728825376,"score_spread":0.1863761459606429,"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."}}