{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007817613,0.00007294515,0.00008101117,0.00004428894,0.00004532733,0.0000329429,0.00008582364,0.00003376768,0.00003542278],"category_scores_gemma":[6.818441e-7,0.00004544658,0.00002583081,0.00002401005,0.00002273733,0.00007167136,0.00003521146,0.00001845532,0.00001177903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001083858,"about_ca_system_score_gemma":0.000002390073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008631516,"about_ca_topic_score_gemma":0.0000420225,"domain_scores_codex":[0.9996032,0.000007264538,0.0001292571,0.00008224189,0.0000379593,0.0001401138],"domain_scores_gemma":[0.999691,0.00005103707,0.000009572917,0.0001749148,0.00001633484,0.00005713071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003693126,0.00005051952,0.01254321,0.0007039337,0.0003720736,0.000004180582,0.0007817388,0.0002107919,0.5567303,0.0148464,0.001964529,0.4117554],"study_design_scores_gemma":[0.003845705,0.0002177479,0.00641089,0.0003643503,0.00007578683,0.00009518883,0.0006416491,0.09854545,0.03066769,0.01480742,0.8429404,0.001387682],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03594416,0.00009950803,0.959683,0.0005603086,0.00005076867,0.0001658905,0.000009111084,0.0002840546,0.003203208],"genre_scores_gemma":[0.9958889,0.0001339163,0.002659101,0.00005013813,0.00002658839,0.00004198106,0.000002396424,0.00001933402,0.001177656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9599447,"threshold_uncertainty_score":0.1853258,"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."}}