{"id":"W1983890061","doi":"10.1109/lsp.2012.2235430","title":"Decentralized Conditional Posterior Cramér–Rao Lower Bound for Nonlinear Distributed Estimation","year":2012,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Estimator; Computer science; Upper and lower bounds; Algorithm; Representation (politics); Mathematics; Mathematical optimization; Statistics","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":[0.0003156271,0.0002204579,0.0002032107,0.00007759251,0.0004423704,0.0005355775,0.0004759181,0.00009651059,0.00003557084],"category_scores_gemma":[0.0000361032,0.000209338,0.00009888522,0.0002542496,0.0001055812,0.001390748,0.00004985221,0.0001723306,0.00004134484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006911797,"about_ca_system_score_gemma":0.0000643627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004098208,"about_ca_topic_score_gemma":4.03681e-7,"domain_scores_codex":[0.9982111,0.00005248182,0.0003549443,0.000367035,0.0003758272,0.0006385609],"domain_scores_gemma":[0.9990452,0.000184264,0.0001986908,0.0002472854,0.0001325673,0.0001920195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008113542,0.00162355,0.002322338,0.000733172,0.0002287666,0.00008540494,0.002381167,0.0797574,0.2439081,0.002423701,0.285405,0.38032],"study_design_scores_gemma":[0.001628787,0.00009566331,0.001285828,0.0001948475,0.00005208391,0.0001170567,0.00001507741,0.9535751,0.01097418,0.0009284007,0.03039945,0.0007335127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08897523,0.000137534,0.9070061,0.002242666,0.0009894851,0.0002196451,0.000174312,0.0002446247,0.00001035418],"genre_scores_gemma":[0.8479694,0.000001639027,0.1472613,0.003547828,0.0005602944,0.00003468749,0.0005882041,0.0000226668,0.00001398047],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8738177,"threshold_uncertainty_score":0.8536556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02000648603562175,"score_gpt":0.2711232236546598,"score_spread":0.2511167376190381,"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."}}