{"id":"W2105693703","doi":"10.1109/tsp.2006.887568","title":"Error Analysis of a Localization Algorithm for Finite-Duration Events","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Monte Carlo method; Algorithm; Bandwidth (computing); Energy (signal processing); Variance (accounting); Computer science; Position (finance); Expression (computer science); Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004035775,0.0005420437,0.0006385323,0.0007876337,0.0004506703,0.001195956,0.001177272,0.001073241,0.001064062],"category_scores_gemma":[0.02640083,0.0003987985,0.0004654136,0.0005849604,0.0009830513,0.001650634,0.001286739,0.0008478001,0.0002760202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016746,"about_ca_system_score_gemma":0.001028223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001891586,"about_ca_topic_score_gemma":0.0009171063,"domain_scores_codex":[0.9976973,0.000557084,0.0001116905,0.0003539909,0.001147265,0.0001327332],"domain_scores_gemma":[0.9829244,0.01355081,0.0008490647,0.0008099155,0.001707747,0.0001581134],"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":[0.0003757451,0.0000325061,0.002268089,0.00007249099,0.00006820193,0.00009641028,0.0001286704,0.9113904,0.008826491,0.03029992,0.000274101,0.04616693],"study_design_scores_gemma":[0.000006490769,0.0000315078,0.000414202,0.000005601217,0.000005362696,0.00004239833,0.000006724224,0.9944687,0.00219843,0.002668634,0.0001427691,0.000009207172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03442476,0.0001605196,0.9646204,0.00008405218,0.00002116376,0.00001147047,0.00001558165,0.0001489195,0.0005130738],"genre_scores_gemma":[0.790235,0.0001972894,0.2073021,0.00005774109,0.00005433194,0.00006954245,0.0001369501,0.0001832878,0.001763791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004035775,"threshold_uncertainty_score":0.02134347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02555956947572719,"score_gpt":0.2936292682509715,"score_spread":0.2680696987752443,"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."}}