{"id":"W4403093553","doi":"10.1016/j.oceaneng.2024.119380","title":"Assessment of the effectiveness of ship machinery noise reduction measures using a test platform in a water basin","year":2024,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Maritime; Université de Sherbrooke","funders":"","keywords":"Marine engineering; Reduction (mathematics); Structural basin; Test (biology); Noise reduction; Engineering; Noise (video); Environmental science; Geology; Computer science; Artificial intelligence; Mathematics; Geomorphology","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":[0.0006443344,0.00009444419,0.0001426138,0.00003871943,0.00001912663,0.000008373417,0.0001038956,0.00002414986,0.00004023189],"category_scores_gemma":[0.00004763145,0.00006025801,0.00005579434,0.0002372554,0.00003135256,0.0001083001,0.0001975676,0.0001132891,0.000001513296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000243489,"about_ca_system_score_gemma":0.00000648358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008336817,"about_ca_topic_score_gemma":0.00003695994,"domain_scores_codex":[0.9993467,0.00002712352,0.0001614556,0.0001395654,0.0001889317,0.0001362292],"domain_scores_gemma":[0.9997637,0.00007982203,0.00001790989,0.0001184462,0.000004004381,0.00001612806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001112061,0.00006065671,0.3908711,0.0009518422,0.00002928855,0.000005307192,0.0002308931,0.1732287,0.4328316,0.0001888218,0.00001236298,0.001578431],"study_design_scores_gemma":[0.00009868054,0.00002758516,0.8868945,0.0004462087,0.0000187949,0.000009565503,0.00001641343,0.07260806,0.03957892,0.00004297906,0.0001661848,0.00009205539],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980306,0.00009240465,0.0003948391,0.00001047167,0.0001405179,0.0001604738,0.000002021657,0.00001830183,0.001150344],"genre_scores_gemma":[0.9995512,0.00001087359,0.000390781,0.000001998006,0.00001551467,0.000003187989,5.853566e-7,0.00001375068,0.000012114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4960235,"threshold_uncertainty_score":0.245725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468554561257768,"score_gpt":0.2341188939428437,"score_spread":0.219433348330266,"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."}}