{"id":"W2127668673","doi":"10.1109/glocom.2009.5425505","title":"Anonymous Cardinality Estimation in RFID Systems with Multiple Readers","year":2009,"lang":"en","type":"article","venue":"","topic":"RFID technology advancements","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Interrogation; Cardinality (data modeling); Computer science; Algorithm; Population; Estimation; Variance (accounting); Radio-frequency identification; Statistics; Data mining; Mathematics; Computer security; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000618547,0.00008560717,0.0001152287,0.00008101563,0.00001435355,0.000009097634,0.00006559848,0.00006314051,0.00000375117],"category_scores_gemma":[0.0000144571,0.0000770174,0.000008557675,0.000174061,0.00001550781,0.0001402629,0.00000387832,0.00009504914,0.00001581742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009940158,"about_ca_system_score_gemma":0.000003863179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006432427,"about_ca_topic_score_gemma":0.00005903986,"domain_scores_codex":[0.9995199,0.000007747156,0.0001250819,0.0001084461,0.00007954791,0.0001592679],"domain_scores_gemma":[0.9997571,0.00001233237,0.00001330338,0.0001855872,0.00001223757,0.00001943417],"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":[0.0000102418,0.00002259132,0.03395803,0.00003464625,0.00001797592,0.00002108152,0.00009632435,0.957323,0.001466481,0.0005186506,0.0002179191,0.006313037],"study_design_scores_gemma":[0.001206856,0.0001122523,0.09757157,0.00007553747,0.00001019924,0.00002981892,0.0003697252,0.89256,0.006998191,0.0003529193,0.0004257725,0.0002871757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8062707,0.0001762201,0.1846057,0.00009167772,0.0001139153,0.0003387521,0.00000219733,0.001058924,0.0073419],"genre_scores_gemma":[0.9901949,0.000005683027,0.00968262,0.00001416472,0.000005028357,0.00001734104,0.000005565655,0.000008592968,0.00006612266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1839241,"threshold_uncertainty_score":0.3140678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007732782974149602,"score_gpt":0.2144406549888157,"score_spread":0.2067078720146661,"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."}}