{"id":"W2003261109","doi":"10.1109/pimrc.2011.6139897","title":"Localization algorithm performance in ultra low power active RFID based patient tracking","year":2011,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Tracking (education); Position (finance); Non-line-of-sight propagation; Radio channel; Algorithm; Shadow mapping; Line-of-sight; Line (geometry); Channel (broadcasting); Wireless; Computer vision; Location tracking; Power (physics); Range (aeronautics); Real-time computing; Artificial intelligence; Engineering; Telecommunications; Mathematics","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":[0.001166055,0.0003193759,0.0004234738,0.0006055459,0.0002673049,0.001046646,0.0005594973,0.0009003287,0.0008076137],"category_scores_gemma":[0.004850185,0.0001441254,0.0002271781,0.0004916307,0.0003256171,0.0006512328,0.0004191478,0.0002912601,0.0005883485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003478125,"about_ca_system_score_gemma":0.0003689171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118346,"about_ca_topic_score_gemma":0.0006765695,"domain_scores_codex":[0.9992679,0.0002177163,0.00005698632,0.0001325974,0.000240533,0.00008433813],"domain_scores_gemma":[0.9979091,0.001170497,0.0002020031,0.000188615,0.000454787,0.00007497144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00468416,0.0004079943,0.03617388,0.0003702551,0.0002452332,0.0005090344,0.0007999752,0.2651946,0.1290839,0.004309366,0.001992634,0.5562289],"study_design_scores_gemma":[0.0001812743,0.001382413,0.02480056,0.00004174733,0.0001577735,0.001501952,0.0002150421,0.8618062,0.1058122,0.001292478,0.002734762,0.00007377451],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6672979,0.0006365327,0.3266586,0.0001791188,0.00005926506,0.00004134694,0.00007222879,0.0018967,0.003158203],"genre_scores_gemma":[0.9533541,0.000159163,0.04519176,0.00006252545,0.000008445211,0.0000238082,0.00009960069,0.00004194191,0.001058789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001166055,"threshold_uncertainty_score":0.006166816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009106098949039812,"score_gpt":0.1788965868145147,"score_spread":0.1697904878654749,"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."}}