{"id":"W4413743465","doi":"10.1016/j.apacoust.2025.111037","title":"Corrigendum to “Data-based modeling of propeller tip-vortex cavitation noise for realistic acoustic ship signature” [Appl. Acoust. 241 (2026) 111004]","year":2025,"lang":"en","type":"article","venue":"Applied Acoustics","topic":"Cavitation Phenomena in Pumps","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nexen (Canada)","funders":"Defense Acquisition Program Administration; Ministry of Oceans and Fisheries; Korea Institute of Marine Science and Technology promotion; Korea University; Korea Research Institute for Defense Technology Planning and Advancement","keywords":"Acoustics; Propeller; Vortex; Signature (topology); Noise (video); Cavitation; Physics; Marine engineering; Engineering; Computer science; Mechanics; Mathematics; Geometry; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005447796,0.0004407123,0.000528221,0.0004205249,0.0001401763,0.00007420697,0.0007214466,0.000278855,0.0000404881],"category_scores_gemma":[0.0004466219,0.0004849882,0.00008371435,0.0007822749,0.00006029559,0.0001047116,0.0001348073,0.0003533387,0.00003647056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003586256,"about_ca_system_score_gemma":0.000321426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002750496,"about_ca_topic_score_gemma":0.00003499986,"domain_scores_codex":[0.9973971,0.0000201004,0.0008449443,0.0006292292,0.0005199242,0.0005886802],"domain_scores_gemma":[0.997694,0.000385912,0.000132791,0.001062127,0.0005481699,0.0001769918],"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":[0.000106704,0.0000750035,0.000005344678,0.0008938893,0.00008561523,0.000001578873,0.0001849014,0.8288051,0.1444301,0.001376499,0.02249222,0.001543084],"study_design_scores_gemma":[0.0009369346,0.00005323724,0.00008317824,0.0001131507,0.0004273599,5.686996e-7,0.0003604715,0.9923162,0.002245932,0.002272163,0.0007109368,0.0004798694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005753889,0.0001069526,0.984728,0.00007377165,0.001563858,0.002188251,0.001070007,0.0004284077,0.004086827],"genre_scores_gemma":[0.9557866,0.00001055829,0.04163152,0.0003576165,0.0001979527,0.0004668072,0.0009167508,0.0001390032,0.0004931852],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9500327,"threshold_uncertainty_score":0.9997602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03398325183523403,"score_gpt":0.2655308162281288,"score_spread":0.2315475643928948,"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."}}