{"id":"W3208816471","doi":"10.1109/access.2021.3122399","title":"Capacity Analysis of NOMA-Enabled Underwater VLC Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Noma; Underwater; Computer science; Transmission (telecommunications); Underwater acoustic communication; Orthogonal frequency-division multiple access; Benchmark (surveying); Channel capacity; Context (archaeology); Computer network; Spectral efficiency; Electronic engineering; Telecommunications; Orthogonal frequency-division multiplexing; Engineering; Telecommunications link; Channel (broadcasting); Geography","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":[0.001395114,0.0007813471,0.0006804759,0.001260163,0.000678207,0.001274141,0.0009353831,0.0007284262,0.002249834],"category_scores_gemma":[0.006507207,0.0003327064,0.000420231,0.001140165,0.001348908,0.001284122,0.001372343,0.0009173156,0.0003106235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002247348,"about_ca_system_score_gemma":0.001096429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005474942,"about_ca_topic_score_gemma":0.002946205,"domain_scores_codex":[0.9990458,0.0003185254,0.00002297132,0.00007760966,0.0002473211,0.00028766],"domain_scores_gemma":[0.9947919,0.003685672,0.0004432749,0.0002244086,0.0007333721,0.0001213815],"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":[0.00006461096,0.00002922985,0.0006986381,0.0001012787,0.00002823603,0.0002055797,0.00008681505,0.9406672,0.003250438,0.04807477,0.001190744,0.005602519],"study_design_scores_gemma":[0.000001627454,0.00001316303,0.0002231906,0.00001199587,0.000004697928,0.0000416551,0.00003332783,0.9918355,0.000478586,0.007064599,0.0002822979,0.000009402905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2704613,0.004633861,0.664912,0.001470513,0.0001431414,0.0001411444,0.0008983436,0.0004551386,0.05688452],"genre_scores_gemma":[0.9897417,0.001036805,0.006804821,0.00009194734,0.00005859396,0.00008707608,0.0001402495,0.00003015281,0.002008766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005474942,"threshold_uncertainty_score":0.01630574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03689284167652253,"score_gpt":0.2656547161609791,"score_spread":0.2287618744844566,"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."}}