{"id":"W4200592094","doi":"10.1049/sil2.12091","title":"Underwater source localization using time difference of arrival and frequency difference of arrival measurements based on an improved invasive weed optimization algorithm","year":2021,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Cramér–Rao bound; Algorithm; Computer science; Position (finance); Mean squared error; Time of arrival; Underwater; Noise (video); Gaussian; Gaussian noise; Upper and lower bounds; Mathematics; Control theory (sociology); Estimation theory; Artificial intelligence; Statistics; Physics; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0001212745,0.0002275053,0.0003051508,0.0001635968,0.0001151403,0.00006547129,0.000140987,0.0001694242,0.00003689007],"category_scores_gemma":[0.00007234547,0.0002214215,0.00003763166,0.0003634066,0.0001234028,0.0002095471,0.00003354505,0.0001323638,4.335876e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007440441,"about_ca_system_score_gemma":0.0001308036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002248774,"about_ca_topic_score_gemma":0.000004844138,"domain_scores_codex":[0.9986832,0.00006825966,0.000409618,0.0002845855,0.0003315317,0.0002228741],"domain_scores_gemma":[0.9991962,0.00004303062,0.000165151,0.0001745288,0.0003633042,0.00005780904],"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":[0.00001169085,0.00004404776,0.001012509,0.0002475123,0.0000152026,0.000001403382,0.0002345338,0.6117644,0.3734578,0.000003276517,4.186544e-7,0.01320719],"study_design_scores_gemma":[0.0003462271,0.00006804147,0.0001572374,0.0002748795,0.00002949009,0.000002056654,0.0001253202,0.6426554,0.3559415,0.0002497624,2.124388e-7,0.0001498072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09448423,0.0001131401,0.9049444,0.000006702066,0.00003738952,0.0001570521,0.00001278453,0.0001632176,0.00008105407],"genre_scores_gemma":[0.9696331,0.000005532485,0.03019019,0.00003139942,0.00002361296,0.000007542504,0.00005948315,0.00003934821,0.000009801327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8751488,"threshold_uncertainty_score":0.9029305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212434612222408,"score_gpt":0.2251050302231163,"score_spread":0.2038615690008755,"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."}}