{"id":"W2156472550","doi":"10.1109/tsmcb.2003.818555","title":"Localization-Based Sensor Validation Using the Kullback–Leibler Divergence","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Unobservable; Divergence (linguistics); Metric (unit); Kullback–Leibler divergence; Mathematics; Event (particle physics); Probability distribution; Function (biology); Likelihood function; Statistics; Algorithm; Estimation theory; Physics","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":[0.01194779,0.001408058,0.002320974,0.002025893,0.001085366,0.002656572,0.002421577,0.002028722,0.0009085962],"category_scores_gemma":[0.03965705,0.0006001444,0.001217276,0.001472818,0.002374636,0.004457571,0.00380566,0.001975016,0.0003563783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001620008,"about_ca_system_score_gemma":0.002209853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001750993,"about_ca_topic_score_gemma":0.001205752,"domain_scores_codex":[0.9921377,0.003568086,0.0007191426,0.0008841097,0.002402987,0.000287996],"domain_scores_gemma":[0.9763778,0.01492658,0.002222058,0.00195193,0.004172596,0.0003490048],"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":[0.0003014972,0.0000924818,0.004622422,0.0002918898,0.0002036233,0.0003572603,0.000252076,0.7680485,0.009739065,0.06271353,0.001056231,0.1523214],"study_design_scores_gemma":[0.00001167992,0.00007628281,0.0005056342,0.00002145588,0.00001449727,0.0001381272,0.00002203447,0.9806395,0.003793537,0.01435322,0.0003879654,0.00003603131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004860889,0.0001041858,0.9944502,0.00005149475,0.00001264678,0.00002102697,0.00001340472,0.0001383763,0.0003478379],"genre_scores_gemma":[0.6703477,0.0003291072,0.3274059,0.0001494428,0.00005933674,0.000256455,0.0002342749,0.0001230266,0.001094757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01194779,"threshold_uncertainty_score":0.06318676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02976239992931105,"score_gpt":0.2461557304629946,"score_spread":0.2163933305336836,"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."}}