{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005337722,0.0007443647,0.0009162312,0.001219701,0.0008799771,0.0007516175,0.001062662,0.0008518517,0.0008854142],"category_scores_gemma":[0.02153514,0.0002025066,0.0003678085,0.0009687764,0.0006979433,0.001044723,0.0009097626,0.000358986,0.0001343015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001219694,"about_ca_system_score_gemma":0.001469728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001979757,"about_ca_topic_score_gemma":0.001418662,"domain_scores_codex":[0.9960057,0.002055375,0.0001978847,0.0003144119,0.001153835,0.0002727121],"domain_scores_gemma":[0.9769858,0.01806333,0.001686116,0.0008295539,0.00208849,0.0003468203],"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":[0.0008410985,0.0001872334,0.00496181,0.0001963502,0.0001029536,0.0001242421,0.0001318629,0.9214614,0.007179377,0.00563079,0.0005885353,0.05859429],"study_design_scores_gemma":[0.00002174733,0.0004312143,0.0007486434,0.00001070876,0.00002190654,0.00006764678,0.00006345138,0.9924393,0.004986453,0.0009004569,0.0002939549,0.00001445637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5703977,0.001704412,0.4209129,0.0004190464,0.00008968977,0.0005221036,0.0002302817,0.0006863545,0.005037482],"genre_scores_gemma":[0.9372695,0.0003641227,0.06142091,0.00003914621,0.00001460665,0.0001860917,0.0001615136,0.0000271079,0.0005169652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005337722,"threshold_uncertainty_score":0.02822888,"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."}}