{"id":"W4400894584","doi":"10.1080/09377255.2024.2374655","title":"Evolution of underwater noise emission prediction technology for ship-propulsor combinations in an industry environment","year":2024,"lang":"en","type":"article","venue":"Ship Technology Research","topic":"Aerodynamics and Acoustics in Jet Flows","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Transport Canada; Bundesministerium für Wirtschaft und Klimaschutz","keywords":"Propulsor; Marine engineering; Propulsion; Computational fluid dynamics; Noise (video); Large eddy simulation; Underwater; Aerospace engineering; Turbulence; Engineering; Field (mathematics); Fidelity; Cavitation; Computer science; Acoustics; Meteorology; Geology; Artificial intelligence; Oceanography; Telecommunications; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007760367,0.0006743364,0.0004594265,0.0006161538,0.0002197997,0.0008103576,0.0007834253,0.000637618,0.000664486],"category_scores_gemma":[0.001338048,0.0003088176,0.0005165455,0.0003215417,0.0002454093,0.0008296932,0.0006533748,0.0006118162,0.0004284786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003068934,"about_ca_system_score_gemma":0.0004604593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002570687,"about_ca_topic_score_gemma":0.001287189,"domain_scores_codex":[0.9995744,0.00007607695,0.00002072467,0.0001171004,0.0001856137,0.00002607326],"domain_scores_gemma":[0.9996033,0.0001432985,0.00004121165,0.00004490371,0.0001444568,0.00002281563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002405427,0.000187828,0.01197288,0.0001498664,0.00009412397,0.00015212,0.0001471979,0.5977125,0.09041832,0.003182104,0.0004867347,0.2952558],"study_design_scores_gemma":[0.000003155174,0.00005684778,0.0009755718,0.000007803726,0.00001172954,0.00001774204,0.00001203946,0.9855945,0.01244437,0.0003147624,0.0005519372,0.000009443283],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1817383,0.0005382611,0.812207,0.0001761692,0.00004653431,0.00004897307,0.00009836067,0.001629801,0.003516547],"genre_scores_gemma":[0.7895142,0.0006729417,0.2068707,0.00003710641,0.00003530962,0.00006679344,0.0002533089,0.0001179455,0.002431751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002570687,"threshold_uncertainty_score":0.005111456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03183554312764428,"score_gpt":0.3184376837488136,"score_spread":0.2866021406211693,"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."}}