{"id":"W2893966609","doi":"10.1002/9781118476406.emoe056","title":"Underwater Noise from Large Commercial Ships—International Collaboration for Noise Reduction","year":2017,"lang":"en","type":"other","venue":"Encyclopedia of Maritime and Offshore Engineering","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Canadian Natural Resources","funders":"Johnson and Johnson; National Oceanic and Atmospheric Administration","keywords":"Noise (video); Government (linguistics); Baleen; Business; Environmental science; Environmental resource management; Engineering; Computer science; Fishery; Whale; Biology","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001235451,0.0002394706,0.0003140272,0.00005809501,0.00007644478,0.00003594979,0.0002379276,0.0001846972,0.002835048],"category_scores_gemma":[0.00006632115,0.0002477784,0.00006730825,0.00004201992,0.00005227772,0.0001346383,0.0003003003,0.0001215208,0.00003379155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000721823,"about_ca_system_score_gemma":0.00001056354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001379509,"about_ca_topic_score_gemma":0.002525875,"domain_scores_codex":[0.999025,0.00001170258,0.0002093491,0.00033078,0.0002142472,0.0002088674],"domain_scores_gemma":[0.9994986,0.00003817999,0.0001621177,0.0002221791,0.00001442353,0.00006452075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006496846,0.0001014689,0.01595229,0.0002136592,0.0002053801,0.000005959229,0.0003451794,0.00009321169,0.0001913614,0.0001609583,0.9572497,0.02541586],"study_design_scores_gemma":[0.0004181874,0.00003275351,0.02405709,0.0001255266,0.00006560556,0.00000113046,0.00002456345,0.0006171188,0.00003737009,0.00004365669,0.9743151,0.0002618658],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006897971,0.001750203,0.002470662,0.0005346453,0.002568674,0.001149704,0.001305733,0.000161338,0.9831611],"genre_scores_gemma":[0.08773768,0.02451386,0.0300624,0.0002100476,0.008263838,0.0004705574,0.002748071,0.001055788,0.8449377],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.1382233,"threshold_uncertainty_score":0.9999974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008417628620535978,"score_gpt":0.2290097369176848,"score_spread":0.2205921082971489,"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."}}