{"id":"W4407640895","doi":"10.1016/j.eswa.2025.126928","title":"Federal underwater acoustic spectrum sensing algorithm based on DCYOLO","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Xidian University; National Natural Science Foundation of China","keywords":"Underwater; Computer science; Spectrum (functional analysis); Algorithm; Acoustic sensor; Acoustics; Artificial intelligence; Geology; Physics; Oceanography","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":[0.0001970728,0.0004292927,0.0005140837,0.0007546232,0.0007511617,0.0005939374,0.0005203164,0.0003908082,0.003000446],"category_scores_gemma":[0.0005727742,0.0001754778,0.0002466083,0.0004347293,0.0002262926,0.000505275,0.000711883,0.0003732649,0.0009002493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000371896,"about_ca_system_score_gemma":0.001598747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00581537,"about_ca_topic_score_gemma":0.01029973,"domain_scores_codex":[0.9997692,0.00001797527,0.00001276155,0.00007547631,0.0000854001,0.0000392395],"domain_scores_gemma":[0.999843,0.00002202883,0.00001182869,0.00002080613,0.000087909,0.00001440561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006954325,0.000188262,0.00477299,0.0001448934,0.00004755844,0.0001450822,0.0001076592,0.03433407,0.1156025,0.01109368,0.01065659,0.8222113],"study_design_scores_gemma":[0.0000756945,0.0001323858,0.003296536,0.00002156244,0.00003631634,0.0001927844,0.00008650984,0.933484,0.04874529,0.002195313,0.01169911,0.00003456605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06905898,0.0005709386,0.9145514,0.0003158452,0.0003933957,0.0001338774,0.0003285138,0.001689437,0.01295764],"genre_scores_gemma":[0.5267643,0.0004027984,0.4552068,0.0002383072,0.0001728403,0.0002692231,0.00115712,0.00009602165,0.01569251],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00581537,"threshold_uncertainty_score":0.011563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009106456601398513,"score_gpt":0.2269556467366524,"score_spread":0.2178491901352539,"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."}}