{"id":"W2117155258","doi":"10.1186/1687-6180-2012-185","title":"Remotely-sensed TOA interpretation of synthetic UWB based on neural networks","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Program for New Century Excellent Talents in University","keywords":"Computer science; Time of arrival; Robustness (evolution); Algorithm; Ultra-wideband; Artificial neural network; Metric (unit); Ranging; Channel (broadcasting); Energy (signal processing); Real-time computing; Artificial intelligence; Telecommunications; 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":[0.0002906551,0.000567322,0.0003108205,0.0006982237,0.0001510175,0.000568938,0.0004065959,0.0004571372,0.0009351796],"category_scores_gemma":[0.001287823,0.0001881866,0.0003622056,0.000517751,0.0002569065,0.0005520394,0.0003188645,0.0003558891,0.0002872027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002936966,"about_ca_system_score_gemma":0.0001961473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001358001,"about_ca_topic_score_gemma":0.001388118,"domain_scores_codex":[0.9998646,0.00004106669,0.000008044804,0.00003446533,0.00003461362,0.00001732341],"domain_scores_gemma":[0.999706,0.0001195918,0.00004095513,0.00002365649,0.0000974975,0.00001223201],"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":[0.000576206,0.0001340428,0.004165658,0.000160654,0.00009115373,0.0004350385,0.0001323732,0.7201838,0.07208791,0.002022343,0.001371884,0.1986389],"study_design_scores_gemma":[0.000005132406,0.00001590802,0.001358824,0.000008004675,0.00001054625,0.00004628103,0.00002069699,0.9935696,0.004232162,0.0005359785,0.0001867981,0.00001009882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5246108,0.0005225134,0.4662087,0.00033121,0.0002071363,0.00004661625,0.0003558873,0.001268868,0.006448275],"genre_scores_gemma":[0.947912,0.0001655257,0.05065091,0.00005151869,0.00003429065,0.00002456255,0.0002257569,0.00004718122,0.0008883154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001358001,"threshold_uncertainty_score":0.003128469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009132804830651226,"score_gpt":0.2566937253094969,"score_spread":0.2475609204788457,"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."}}