{"id":"W4412908762","doi":"10.2166/hydro.2025.288","title":"Enhancing object detection in underwater aquatic system with YOLO-transformer hybrid model and IoT sensor integration","year":2025,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Underwater; Computer science; Transformer; Real-time computing; Environmental science; Artificial intelligence; Engineering; Geology; Electrical engineering; Oceanography","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":[0.000137177,0.000448322,0.000310956,0.0002826912,0.0001431741,0.0004390093,0.0006670624,0.0004194045,0.001082632],"category_scores_gemma":[0.00026916,0.0002017392,0.0004410746,0.0001903743,0.0002268168,0.0005837793,0.0005656155,0.0003532525,0.0003345279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004194271,"about_ca_system_score_gemma":0.0004579792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008177543,"about_ca_topic_score_gemma":0.00973558,"domain_scores_codex":[0.9999299,0.000006477644,0.000002600716,0.00002458293,0.00001824696,0.00001819907],"domain_scores_gemma":[0.9999337,0.0000150185,0.000008820239,0.000007453662,0.00002802839,0.000007020026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003283958,0.0002333344,0.005598983,0.000132278,0.0001335436,0.0002756331,0.0001461232,0.6619279,0.1038293,0.005006564,0.002439519,0.2199485],"study_design_scores_gemma":[0.000002106981,0.00002489013,0.0003191831,0.000002065133,0.0000101425,0.00001443848,0.000004699165,0.9960927,0.002992845,0.0002908247,0.000242786,0.000003297757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1422961,0.0004593147,0.8497985,0.0002147345,0.00009481035,0.0000493525,0.0001165229,0.001812517,0.00515817],"genre_scores_gemma":[0.9480875,0.0002168007,0.047334,0.0001369557,0.00001791595,0.00004022349,0.0001383242,0.00005009552,0.003978106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008177543,"threshold_uncertainty_score":0.01625991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068792576573151,"score_gpt":0.2267084273229783,"score_spread":0.2160205015572468,"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."}}