{"id":"W2117935708","doi":"10.1109/glocom.2009.5425452","title":"Randomized Multi-Channel Interrogation Algorithm for Large-Scale RFID Systems","year":2009,"lang":"en","type":"article","venue":"","topic":"RFID technology advancements","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interrogation; Computer science; Correctness; Radio-frequency identification; Channel (broadcasting); Network packet; Heuristic; Collision; Algorithm; Set (abstract data type); Collision problem; Real-time computing; Distributed computing; Computer network; Computer security","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":[0.002843956,0.0007743753,0.001358067,0.0004969315,0.0007792531,0.001042448,0.002023911,0.001151481,0.001706045],"category_scores_gemma":[0.006896846,0.0005059196,0.0005132958,0.000723173,0.001224962,0.001460168,0.001244029,0.001212909,0.0004130916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001538792,"about_ca_system_score_gemma":0.002268954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002000498,"about_ca_topic_score_gemma":0.001900175,"domain_scores_codex":[0.9979394,0.000857515,0.0000843626,0.0003606888,0.00041215,0.0003458603],"domain_scores_gemma":[0.9940562,0.004169655,0.0006462974,0.0004894689,0.0004397855,0.0001986047],"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":[0.0002497293,0.00007741886,0.000426058,0.00005080227,0.00003244626,0.00005457824,0.0000714348,0.9565564,0.002215805,0.01577037,0.001085122,0.0234099],"study_design_scores_gemma":[0.00002899841,0.00002467453,0.00003286022,0.000001748244,0.000003833768,0.00001385457,0.000005457944,0.996765,0.000346785,0.002581054,0.0001909465,0.000004821002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01377153,0.0001471313,0.9847463,0.0001448296,0.00001808532,0.00005609566,0.00002418864,0.0003639739,0.0007279211],"genre_scores_gemma":[0.6656373,0.0001407164,0.3313825,0.0002141976,0.00003901579,0.0003270319,0.0001301865,0.00009929908,0.002029589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002843956,"threshold_uncertainty_score":0.01504046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142930784132546,"score_gpt":0.2507719157293771,"score_spread":0.2393426078880517,"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."}}