{"id":"W7117600710","doi":"10.1109/mswim67937.2025.11308740","title":"DV-Hop Localization Algorithm Optimized by NSGA-II for UWSNs","year":2025,"lang":"","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Convergence (economics); Particle swarm optimization; Node (physics); Energy consumption; Pareto principle; Energy (signal processing); Optimization problem; Efficient energy use","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"],"consensus_categories":[],"category_scores_codex":[0.0004018812,0.0003552216,0.0004823796,0.0001657807,0.0005293689,0.0002443461,0.0005809377,0.0003057049,0.000316308],"category_scores_gemma":[0.000008242836,0.0003641408,0.0002103584,0.0005127706,0.00004869329,0.0002266219,0.0002414794,0.000177678,0.00004912529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001904431,"about_ca_system_score_gemma":0.00006463631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001859557,"about_ca_topic_score_gemma":0.00001578435,"domain_scores_codex":[0.9979595,0.0001048187,0.0008930664,0.0003854926,0.0001881508,0.0004689851],"domain_scores_gemma":[0.9985669,0.0001625001,0.00009986498,0.000808903,0.0002438134,0.0001180306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001689936,0.0008527766,0.00009398379,0.001442326,0.001876631,0.000001161682,0.00355039,0.1270009,0.01678909,0.01142084,0.2378334,0.5989695],"study_design_scores_gemma":[0.001620545,0.00004771703,0.000001144545,0.0001067323,0.00006178369,0.000001044509,0.0004101678,0.5864443,0.01418433,0.000417735,0.3964604,0.0002441576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001314111,0.003291546,0.982928,0.001551737,0.0004782634,0.001092112,0.00008337229,0.0003543752,0.01008919],"genre_scores_gemma":[0.5623435,0.00313419,0.3031665,0.001512664,0.0002772813,0.0008645029,0.0006590832,0.0001960181,0.1278462],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6797615,"threshold_uncertainty_score":0.999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090109885250365,"score_gpt":0.242411662380856,"score_spread":0.2315105635283523,"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."}}