{"id":"W4390743887","doi":"10.3389/fmars.2023.1331635","title":"A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization","year":2024,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Polit National Laboratory for Marine Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Initialization; Artificial neural network; Multipath propagation; Underwater acoustic communication; Equalization (audio); Channel (broadcasting); Algorithm; Control theory (sociology); Mathematical optimization; Underwater; Mathematics; Artificial intelligence; Telecommunications","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":[0.0002941054,0.0003656648,0.0002847492,0.000215324,0.0002155286,0.0003240411,0.0005988483,0.0005203012,0.001383536],"category_scores_gemma":[0.0006366598,0.0001511676,0.0001951891,0.0002348197,0.0003568728,0.0005069219,0.0003795032,0.0004813867,0.0002469149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004669867,"about_ca_system_score_gemma":0.0006623751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0034972,"about_ca_topic_score_gemma":0.004467585,"domain_scores_codex":[0.9998423,0.00003843092,0.000009344538,0.00003354268,0.00005304792,0.00002340799],"domain_scores_gemma":[0.9998749,0.00004199235,0.0000183564,0.00001385411,0.0000442891,0.000006466987],"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":[0.000177505,0.00006773528,0.001109089,0.00006374244,0.00004199459,0.0000932136,0.00004970851,0.7811021,0.02181258,0.007689844,0.001008806,0.1867837],"study_design_scores_gemma":[0.000005184939,0.00002120083,0.00009384544,0.000002476829,0.000004037591,0.00001221197,0.000001981383,0.9964412,0.002636552,0.0003706295,0.0004072586,0.000003418275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07240795,0.0006662148,0.9219558,0.0001489768,0.00007113743,0.0000407354,0.00002586476,0.0004276378,0.004255766],"genre_scores_gemma":[0.8994717,0.0003088083,0.0943572,0.00009614344,0.00003242433,0.00005931275,0.00006724375,0.00002172683,0.005585529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0034972,"threshold_uncertainty_score":0.006953716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017812281752759,"score_gpt":0.2449009140658236,"score_spread":0.2247227912482961,"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."}}