{"id":"W2130159201","doi":"10.1109/whcm.2010.5441284","title":"Uncertainties inherent in RFID tracking systems in an emergency department","year":2010,"lang":"en","type":"article","venue":"","topic":"RFID technology advancements","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Emergency department; Tracking (education); Tracking system; Real-time computing; Artificial intelligence; Kalman filter; Medicine","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":[0.0001112962,0.0001224975,0.0001382789,0.000197676,0.00001436281,0.00001103037,0.0001601789,0.0001114824,0.0001562922],"category_scores_gemma":[0.00001199231,0.0001223862,0.00001359963,0.0002033137,0.00001535917,0.0002210771,0.00002241905,0.0002855305,0.00002445941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008126628,"about_ca_system_score_gemma":0.000005211126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001180009,"about_ca_topic_score_gemma":0.009453541,"domain_scores_codex":[0.9991813,0.00001181778,0.00029888,0.0001543299,0.0000883692,0.0002652469],"domain_scores_gemma":[0.9996846,0.000006934536,0.00001808674,0.000244187,0.00001400176,0.00003213063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007209447,0.0001915947,0.7973971,0.0001197048,0.0000254146,0.00004190419,0.0004654215,0.1023436,0.06677203,0.009271839,0.0002710944,0.02309307],"study_design_scores_gemma":[0.002665347,0.0002322018,0.6733093,0.0001779916,0.00001577184,0.00002208527,0.002817397,0.2472085,0.04977291,0.00516634,0.01710846,0.001503684],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955878,0.0002679708,0.0006601049,0.0000181658,0.001233437,0.0002698316,0.000001635029,0.0003234271,0.001637618],"genre_scores_gemma":[0.9991672,0.00005739697,0.0005183819,0.000003752831,0.00002273685,0.0001367457,0.000006050699,0.00002007672,0.00006765551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1448649,"threshold_uncertainty_score":0.5275297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570366966516006,"score_gpt":0.261964408553861,"score_spread":0.2462607388887009,"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."}}