{"id":"W1969578102","doi":"10.1016/j.aei.2006.09.002","title":"A proximity-based method for locating RFID tagged objects","year":2007,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Radio-frequency identification; Software deployment; Identification (biology); Computer science; Field (mathematics); Radio frequency; Tracking (education); Power (physics); Real-time computing; Electronic engineering; Engineering; Embedded system; Telecommunications; Software engineering; Computer security; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003918938,0.0002383315,0.0002307984,0.0002641026,0.00007326069,0.0000333171,0.0001987424,0.0001518573,0.000002849348],"category_scores_gemma":[0.0002617846,0.0002513897,0.00007315529,0.0004312752,0.00001589691,0.0003657839,0.0000211446,0.0002127024,0.000008243283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001283122,"about_ca_system_score_gemma":0.00001978785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.044526e-7,"about_ca_topic_score_gemma":0.000002664643,"domain_scores_codex":[0.9987069,0.000002308641,0.000546451,0.00008307205,0.0001550242,0.0005062573],"domain_scores_gemma":[0.9992604,0.0002297693,0.00006742996,0.0002714443,0.00009918572,0.000071792],"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":[0.000006813203,0.000005288164,0.00001601008,0.0006619038,0.00001838349,7.895447e-7,0.0004559505,0.9616834,0.002436594,0.002287924,0.00004524054,0.03238172],"study_design_scores_gemma":[0.0005431855,0.00003605753,0.00002636076,0.00007064801,0.00001094651,0.000003724216,0.0003263075,0.8201119,0.1695011,0.0001042721,0.008985672,0.0002798159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004993348,0.00007371793,0.9908137,0.000008901746,0.0003847036,0.0004522342,0.00001005093,0.00241059,0.0008527591],"genre_scores_gemma":[0.3145067,0.000006455371,0.6852269,0.00007476348,0.00003451227,0.00006479891,0.00002615929,0.00004884051,0.00001089009],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3095133,"threshold_uncertainty_score":0.9999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005400104350427364,"score_gpt":0.2361666419198142,"score_spread":0.2307665375693868,"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."}}