{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005876568,0.0006865318,0.001248725,0.0009896241,0.0008239379,0.001795359,0.001659048,0.00125813,0.0005595731],"category_scores_gemma":[0.03405309,0.0006730828,0.0006614137,0.001443962,0.002099859,0.005055756,0.002787138,0.001291955,0.0002728598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091889,"about_ca_system_score_gemma":0.0008821826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001160647,"about_ca_topic_score_gemma":0.0006516709,"domain_scores_codex":[0.9930038,0.003540328,0.000338735,0.001082575,0.00159004,0.0004445593],"domain_scores_gemma":[0.9609074,0.02912793,0.004398481,0.003622587,0.001605399,0.0003382158],"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":[0.0002902096,0.00004244562,0.00371396,0.000108946,0.00008654787,0.0002957034,0.000311733,0.8783733,0.004409103,0.06206499,0.0004823898,0.04982071],"study_design_scores_gemma":[0.000009915238,0.00003446999,0.0004379667,0.000008867744,0.00001430785,0.0001457684,0.0000439436,0.9716144,0.002741599,0.02431834,0.000604108,0.00002620631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02007711,0.0001508874,0.9791743,0.0000857006,0.00001317099,0.0000129062,0.00001982663,0.0000985259,0.0003675724],"genre_scores_gemma":[0.786051,0.0004333048,0.2110465,0.00009680751,0.0001090296,0.0001027194,0.0001334832,0.00007097294,0.001956177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005876568,"threshold_uncertainty_score":0.03107864,"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."}}