{"id":"W4408951699","doi":"10.1109/tmc.2025.3555640","title":"A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Architecture; Underwater acoustic communication; Underwater; Acoustic sensor; Wireless sensor network; Computer network; Computer architecture; Acoustics","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":[0.0001252268,0.0002310294,0.0002387464,0.0001250581,0.0002887057,0.0001709449,0.0002380941,0.000118287,0.000009175514],"category_scores_gemma":[5.545677e-7,0.0002232898,0.0001909867,0.000300631,0.0000345806,0.00004158454,0.000003089735,0.0003363753,0.00001379832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128801,"about_ca_system_score_gemma":0.0000224975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003859508,"about_ca_topic_score_gemma":0.00001937339,"domain_scores_codex":[0.9988578,0.00003892426,0.0003828306,0.0002361631,0.00009596315,0.0003883432],"domain_scores_gemma":[0.9990256,0.0004495057,0.00003592575,0.0003768231,0.00004613476,0.00006594264],"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":[0.00002068524,0.00004195051,0.00001097649,0.00007749529,0.00008257692,5.722363e-7,0.00008142531,0.8908164,0.0003369399,0.000003753252,0.0001069736,0.1084202],"study_design_scores_gemma":[0.000363427,0.00006534054,0.000005471785,0.0002259661,0.00003744613,0.000003327273,0.000105127,0.9800088,0.005124807,0.00007367934,0.0137691,0.0002175126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005928781,0.0001743763,0.9914485,0.00007127566,0.0006136647,0.0006904091,0.00001225659,0.0007631756,0.0002975211],"genre_scores_gemma":[0.9937213,0.000009679712,0.005475624,0.0001835796,0.0001315095,0.000141237,0.00000794175,0.00006054689,0.0002685549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9877926,"threshold_uncertainty_score":0.9105495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114799570969295,"score_gpt":0.2320211881356738,"score_spread":0.2205412310387443,"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."}}