{"id":"W2956846160","doi":"10.1109/icc.2019.8761269","title":"Performance Evaluation of Candidate Set Selection Procedures for Underwater Sensor Networks","year":2019,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Underwater; Computer science; Routing protocol; Wireless sensor network; Underwater acoustic communication; Routing (electronic design automation); Computer network; Set (abstract data type); Channel (broadcasting); Selection (genetic algorithm); Distributed computing; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003124863,0.00007010574,0.00009635423,0.00003946915,0.00002845493,0.00001593067,0.00007362901,0.00004893487,0.00007124666],"category_scores_gemma":[8.893909e-7,0.00005833348,0.00002652024,0.00007812754,0.000005512923,0.0001008232,0.000009480539,0.00004007193,0.00001580208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004980222,"about_ca_system_score_gemma":0.00001933074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001410552,"about_ca_topic_score_gemma":0.00004630872,"domain_scores_codex":[0.9994746,0.00002358485,0.0001761832,0.00007751911,0.0001318164,0.0001162686],"domain_scores_gemma":[0.9996406,0.00001649059,0.00003155211,0.0001391539,0.0001548907,0.0000172876],"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":[0.0000211091,0.000009976528,0.01440755,0.0002485012,0.00006168739,4.517708e-9,0.0002594201,0.9492636,0.03093943,0.00004485922,0.0002785686,0.004465296],"study_design_scores_gemma":[0.0003957056,0.00003600648,0.002378125,0.00002711907,0.00001799286,0.000001985349,0.00007557048,0.9541609,0.04106757,0.00001896935,0.00173863,0.00008146979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703013,0.00005336053,0.02741838,0.00001845056,0.00006078424,0.0005465861,0.000002050177,0.00009066232,0.00150847],"genre_scores_gemma":[0.9985246,0.00002981193,0.0008445683,0.00001384533,0.00003272226,0.00007185266,0.00002507494,0.00001638551,0.0004411411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02822335,"threshold_uncertainty_score":0.237877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439665633487999,"score_gpt":0.2530314752365884,"score_spread":0.2286348189017084,"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."}}