{"id":"W2971359098","doi":"10.1002/aqc.3103","title":"Automated detection and tracking of marine mammals: A novel sonar tool for monitoring effects of marine industry","year":2019,"lang":"en","type":"article","venue":"Aquatic Conservation Marine and Freshwater Ecosystems","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"Natural Environment Research Council; Sight Research UK; Scottish Government; Department of Energy and Climate Change","keywords":"Marine mammal; Sonar; Support vector machine; Marine engineering; Harbor seal; Computer science; Classifier (UML); Fur seal; Marine debris; Artificial intelligence; Engineering; Geology; Oceanography; Fishery; Phoca; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004447805,0.0003085368,0.0003729179,0.001112597,0.0001142214,0.0002799992,0.0004736012,0.0003964274,0.0006748732],"category_scores_gemma":[0.0005818221,0.0002082596,0.000176268,0.000444669,0.0001745442,0.0003792885,0.0003176971,0.0002019938,0.0004873375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057277,"about_ca_system_score_gemma":0.0001421689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000610734,"about_ca_topic_score_gemma":0.001030007,"domain_scores_codex":[0.9997244,0.00004729642,0.00001748785,0.00009168536,0.0001031078,0.00001590538],"domain_scores_gemma":[0.9995499,0.0001135294,0.0001390718,0.00004571103,0.0001283262,0.00002344043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004218626,0.0003416394,0.0735667,0.0002677994,0.0001625799,0.0003047958,0.0002463674,0.01389935,0.5121624,0.0003097286,0.001782302,0.3965344],"study_design_scores_gemma":[0.00009070524,0.001428217,0.3056571,0.00006082273,0.000173849,0.001478686,0.0001654146,0.5508524,0.1338194,0.0004119313,0.005772834,0.00008851487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6969325,0.000620461,0.2978871,0.00009119901,0.00007256646,0.0001715439,0.0007210361,0.002012374,0.001491158],"genre_scores_gemma":[0.7617664,0.0002737233,0.2358432,0.00007250311,0.00007518309,0.0001449405,0.000477734,0.00004718569,0.001299168],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001112597,"threshold_uncertainty_score":0.002352297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791488371779835,"score_gpt":0.2329154668640843,"score_spread":0.2150005831462859,"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."}}