{"id":"W2153592042","doi":"10.1109/oceans.1992.612633","title":"Noise Effects On The IRWLS Algorithm Performance For Time Delay Estimation","year":2005,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Technical University of Nova Scotia","funders":"","keywords":"Computer science; Noise (video); Estimation; Algorithm; Noise measurement; Speech recognition; Noise reduction; Artificial intelligence; Engineering","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":[0.002040026,0.0007389868,0.0006435264,0.0008103696,0.0004428517,0.001120902,0.0006526576,0.0009897089,0.001638876],"category_scores_gemma":[0.01045934,0.0002504041,0.0003338324,0.0008674233,0.000491256,0.001028443,0.0005884761,0.0006022431,0.0006636708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006585898,"about_ca_system_score_gemma":0.0006411921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003286703,"about_ca_topic_score_gemma":0.001906196,"domain_scores_codex":[0.9990508,0.0002296937,0.00006916803,0.0001561752,0.0004150236,0.00007907091],"domain_scores_gemma":[0.9950837,0.003353258,0.0002641036,0.0002942899,0.0009544865,0.00005024145],"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":[0.001272859,0.0001909296,0.01156797,0.0004712424,0.0001296543,0.0002436471,0.0003510974,0.5758096,0.04134748,0.004154724,0.001368974,0.3630918],"study_design_scores_gemma":[0.00002174422,0.000317249,0.003471008,0.00002989819,0.00002243803,0.0001182876,0.00005893279,0.9649473,0.02915852,0.0005692903,0.001245344,0.00004003825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2854919,0.001519715,0.7058514,0.0002824078,0.0001694855,0.0001118654,0.0001829284,0.001993189,0.004397203],"genre_scores_gemma":[0.774868,0.0006623623,0.2207762,0.0001035561,0.00004053987,0.0001300103,0.0004411506,0.0002926713,0.002685508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003286703,"threshold_uncertainty_score":0.01078886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008668488849457518,"score_gpt":0.225609156737518,"score_spread":0.2169406678880605,"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."}}