{"id":"W2743288534","doi":"10.1002/dac.3392","title":"TDMA‐SDMA‐based RFID algorithm for fast detection and efficient collision avoidance","year":2017,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"RFID technology advancements","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Radio-frequency identification; Identification (biology); Collision; Algorithm; Key (lock); Throughput; Collision avoidance; Time division multiple access; Field-programmable gate array; Software deployment; Real-time computing; Wireless; Embedded system; Computer network; Telecommunications; Computer security; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0004684666,0.0000922837,0.0001569295,0.0001834522,0.0002047316,0.000162066,0.0008552914,0.00007558479,0.000001451891],"category_scores_gemma":[0.0001212237,0.00009152266,0.00004876101,0.00003091679,0.00007304621,0.0002545387,0.00006755515,0.0001738627,0.000003020362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720616,"about_ca_system_score_gemma":0.00001686087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000933972,"about_ca_topic_score_gemma":0.000007226484,"domain_scores_codex":[0.9991081,0.00003619586,0.000424664,0.00007221819,0.0002701596,0.00008860781],"domain_scores_gemma":[0.9983005,0.000121882,0.0005212767,0.0004328592,0.0005863617,0.00003713533],"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":[0.0001504524,0.0001644443,0.001895106,0.00004873781,0.0005057339,0.00001036078,0.0003057893,0.5679907,0.02549513,0.001577567,0.0004031568,0.4014528],"study_design_scores_gemma":[0.001567884,0.00006086381,0.003069991,0.0003190021,0.00001591401,0.00005951723,0.0001097953,0.9618406,0.01393713,0.0001640297,0.018739,0.0001163203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1755085,0.002131445,0.8192415,0.0003640167,0.002100852,0.0002634634,0.00002857338,0.00005691004,0.000304664],"genre_scores_gemma":[0.9891717,0.0003535343,0.01027709,0.00001184985,0.0001014763,0.0000316532,0.000004277466,0.00001619846,0.00003222933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8136631,"threshold_uncertainty_score":0.3732186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388367076653268,"score_gpt":0.2865889310193687,"score_spread":0.272705260252836,"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."}}