{"id":"W4385350098","doi":"10.1117/12.2688330","title":"Automated fish detection and classification on sonar images using detection transformer and YOLOv7","year":2023,"lang":"en","type":"article","venue":"","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Sonar; Computer science; Artificial intelligence; Fish <Actinopterygii>; Pattern recognition (psychology); High resolution; Contextual image classification; Object detection; Remote sensing; Fishery; Geography; Biology; Image (mathematics)","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":[0.000782747,0.0008839281,0.0006338052,0.00140656,0.0002767578,0.0006113779,0.0008430647,0.0005855051,0.00226748],"category_scores_gemma":[0.0008363932,0.0004022426,0.0006081429,0.0004517721,0.0002948688,0.0007696247,0.0007950887,0.0004427107,0.001588733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005307288,"about_ca_system_score_gemma":0.0006343355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008292523,"about_ca_topic_score_gemma":0.01187941,"domain_scores_codex":[0.9996777,0.00003456873,0.00001971811,0.0001347907,0.00007633585,0.00005694035],"domain_scores_gemma":[0.9997386,0.00005414654,0.00003480368,0.0000412695,0.0001070751,0.00002417741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00124778,0.0003264888,0.01916957,0.000358892,0.0002789834,0.0003195855,0.0001219452,0.03431305,0.1896285,0.001331492,0.007613872,0.7452899],"study_design_scores_gemma":[0.00005251264,0.0003277257,0.01435456,0.00003861422,0.0001186599,0.0002941968,0.00009515033,0.8964081,0.08288983,0.001020734,0.004354178,0.00004564854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.397199,0.001222775,0.563009,0.0002379679,0.0003990148,0.0003022061,0.001873625,0.02947955,0.006276801],"genre_scores_gemma":[0.744218,0.0004864563,0.2415982,0.0002198571,0.00006876486,0.0001327714,0.004803362,0.0003111468,0.008161374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008292523,"threshold_uncertainty_score":0.01648849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03516496563164586,"score_gpt":0.2962496175132415,"score_spread":0.2610846518815956,"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."}}