{"id":"W2160687055","doi":"10.1109/glocom.2010.5683692","title":"Probabilistic Estimation of Location Error in Wireless Ad Hoc Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robustness (evolution); Probabilistic logic; Computer science; Cramér–Rao bound; Wireless ad hoc network; Upper and lower bounds; Algorithm; Network topology; Scaling; Variance (accounting); Probability density function; Function (biology); Wireless sensor network; Mathematics; Estimation theory; Wireless; Statistics; Artificial intelligence","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":[0.00007552423,0.00006291928,0.00008559573,0.00008859004,0.00001098556,0.000007137329,0.00007970935,0.0001163209,0.00003279384],"category_scores_gemma":[0.00006387725,0.00005921562,0.00001156436,0.0002938465,0.00003786909,0.0000771144,0.00001001391,0.0001276961,0.000006336222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001902522,"about_ca_system_score_gemma":0.000008531459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000509004,"about_ca_topic_score_gemma":0.0002624413,"domain_scores_codex":[0.999597,0.000004296908,0.0001773032,0.00006808819,0.00005647933,0.00009687469],"domain_scores_gemma":[0.9997621,0.00002381497,0.00002011402,0.000141084,0.00004125753,0.00001160999],"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":[0.000003583124,0.0000134374,0.0004497571,0.00006889695,0.000002340859,2.845345e-7,0.00006999527,0.9321721,0.0008791814,0.01025558,0.00007930606,0.05600553],"study_design_scores_gemma":[0.0001180342,0.000009839629,0.004155267,0.00001708088,0.00000249922,7.757722e-7,0.00004228762,0.9869351,0.007707482,0.0008975464,0.00004475444,0.00006934803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6561182,0.00006474851,0.3421791,0.0000284908,0.0002168577,0.0001561882,5.757248e-7,0.0003910577,0.0008447839],"genre_scores_gemma":[0.9974838,0.00001942123,0.002419288,0.000005740776,0.000008062406,0.00001904983,0.00001061431,0.00001030776,0.00002367355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3413656,"threshold_uncertainty_score":0.2414743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006371265075601369,"score_gpt":0.214783938478151,"score_spread":0.2084126734025496,"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."}}