{"id":"W4410495098","doi":"10.1016/j.future.2025.107880","title":"Underwater acoustic intelligent spectrum sensing with multimodal data fusion: An Mul-YOLO approach","year":2025,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; Hubei University of Arts and Science; National Natural Science Foundation of China","keywords":"Computer science; Underwater; Sensor fusion; Spectrum (functional analysis); Electromagnetic spectrum; Remote sensing; Artificial intelligence; Acoustics; Optics; Oceanography; Geology; Physics","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":[0.0005466932,0.0002746191,0.0002845193,0.0002446845,0.0004156177,0.0008522379,0.0006648319,0.0001530604,0.00007692339],"category_scores_gemma":[0.000003454664,0.0001935393,0.00002839919,0.0003666419,0.00006463435,0.0004213172,0.0001291166,0.0002902835,0.0000665041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003154046,"about_ca_system_score_gemma":0.0001779084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001419182,"about_ca_topic_score_gemma":0.004221134,"domain_scores_codex":[0.9974127,0.0003608665,0.0003758128,0.0008448532,0.0005463754,0.0004593516],"domain_scores_gemma":[0.9985314,0.00005778165,0.0000782287,0.001023077,0.0001339655,0.0001755657],"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":[0.00004217492,0.00004844962,0.002138633,0.0001231317,0.00008852941,0.0000369299,0.0004241887,0.9641168,0.0003162754,0.00002331987,0.02014381,0.01249783],"study_design_scores_gemma":[0.0003024351,0.0001529002,0.0008898251,0.00003469802,0.00002867911,0.00009323107,0.0002955972,0.9855622,0.000185201,0.000004716499,0.01220521,0.0002452791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009385162,0.0003122893,0.9757956,0.0003559509,0.01281324,0.0005951329,0.00009867006,0.0001200295,0.0005239223],"genre_scores_gemma":[0.8499643,0.00002473006,0.1102377,0.0003244015,0.03398796,0.000002405436,0.004729229,0.00001558177,0.0007136972],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.865558,"threshold_uncertainty_score":0.821815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04521145810799131,"score_gpt":0.2618626491299501,"score_spread":0.2166511910219587,"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."}}