{"id":"W2561870777","doi":"10.1109/imis.2016.136","title":"Geographic and Opportunistic Clustering for Underwater WSNs","year":2016,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Cluster analysis; Computer science; Network packet; Energy consumption; Computer network; Routing protocol; Underwater; Wireless sensor network; Node (physics); Swarm behaviour; Real-time computing; Geography; Engineering; Artificial intelligence; Electrical engineering","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":[0.0003472687,0.0002522098,0.0002636467,0.0004681227,0.000506854,0.0003550651,0.0007577697,0.0003426936,0.0006052788],"category_scores_gemma":[0.0008449527,0.0001500653,0.0002875038,0.0007004184,0.0003571949,0.0006678324,0.0008132643,0.0002641817,0.0002022118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005031866,"about_ca_system_score_gemma":0.0006720053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00371866,"about_ca_topic_score_gemma":0.005316286,"domain_scores_codex":[0.9997097,0.00008043945,0.00001410071,0.00005317413,0.0001133123,0.00002929249],"domain_scores_gemma":[0.9996951,0.0000870077,0.0000508636,0.00007461003,0.00007175757,0.00002070157],"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":[0.00007875804,0.00005189242,0.00295452,0.0004338829,0.0001228254,0.0006142103,0.0003258895,0.5130225,0.02310143,0.1682071,0.0106759,0.2804111],"study_design_scores_gemma":[0.00001219219,0.00008926553,0.001490946,0.00002967901,0.00003899012,0.000565147,0.0002548484,0.9026543,0.005236974,0.03811524,0.05146816,0.00004416095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02207082,0.00163182,0.9673871,0.0004824785,0.0001712237,0.0001102134,0.0001076922,0.0006097101,0.007428893],"genre_scores_gemma":[0.7223107,0.002669032,0.264729,0.0002212484,0.0001445888,0.0002596238,0.0003444761,0.00009509064,0.00922632],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00371866,"threshold_uncertainty_score":0.007394075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02822445596913861,"score_gpt":0.2202151924744,"score_spread":0.1919907365052614,"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."}}