{"id":"W4416750031","doi":"10.1109/iros60139.2025.11246089","title":"The Common Objects Underwater (COU) Dataset for Robust Underwater Object Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Environment and Natural Resources; National Science Foundation","keywords":"Underwater; Object (grammar); Object detection; Focus (optics); Class (philosophy); Field (mathematics)","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":[0.00114687,0.003735406,0.001819164,0.004131285,0.001101813,0.001656595,0.003625828,0.002564953,0.005009034],"category_scores_gemma":[0.002887277,0.0007808972,0.002085933,0.003050487,0.000843393,0.001787522,0.002993048,0.001808247,0.008391917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288195,"about_ca_system_score_gemma":0.001563448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02469672,"about_ca_topic_score_gemma":0.05430456,"domain_scores_codex":[0.9984651,0.0001356536,0.0001446759,0.0004739611,0.0005354363,0.0002454073],"domain_scores_gemma":[0.9990407,0.0001604996,0.0001172268,0.0003141144,0.0002599997,0.0001074742],"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":[0.0009948732,0.001222199,0.01484452,0.004570647,0.0008184822,0.001177739,0.0003629979,0.0230824,0.03560429,0.001924822,0.6708755,0.2445216],"study_design_scores_gemma":[0.0006738544,0.00121301,0.08793242,0.001052832,0.0005706431,0.005321228,0.001631822,0.2193632,0.08376648,0.004981532,0.5929476,0.0005454874],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1268893,0.005706717,0.06050192,0.001080915,0.001260074,0.002413021,0.7300817,0.0539945,0.01807171],"genre_scores_gemma":[0.04311751,0.0005758625,0.05249418,0.0002513519,0.00006955027,0.0006320365,0.8991399,0.001023681,0.002695848],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02469672,"threshold_uncertainty_score":0.04910594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02731659787627732,"score_gpt":0.2917617233080821,"score_spread":0.2644451254318048,"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."}}