{"id":"W2143921303","doi":"10.1109/wcnc.2013.6554576","title":"Efficient multi-receiver message aggregation for short message delivery in M2M networks","year":2013,"lang":"en","type":"article","venue":"","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Acknowledgement; Network packet; Automatic repeat request; Computer network; Reliability (semiconductor); NAK; Transmission (telecommunications); Heuristic; Scheme (mathematics); Selective Repeat ARQ; Machine to machine; Wireless; Integer programming; Distributed computing; Hybrid automatic repeat request; Algorithm; Internet of Things; Telecommunications; Computer security","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":[0.0002079603,0.0001752231,0.0001847096,0.00007372944,0.00004784068,0.00006060118,0.0001264035,0.000156096,0.0003072743],"category_scores_gemma":[0.000007782929,0.0001579002,0.00007036059,0.0001693829,0.00001613324,0.0001017255,0.00002572415,0.000154765,0.00005027492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007097826,"about_ca_system_score_gemma":0.000006660044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006797146,"about_ca_topic_score_gemma":0.0001052833,"domain_scores_codex":[0.9990411,0.00002190549,0.0002676903,0.0001881831,0.00009701617,0.0003840649],"domain_scores_gemma":[0.9995996,0.00009480822,0.00001731023,0.0001688402,0.00005016446,0.00006928353],"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":[0.000008117972,0.0000306488,0.0006173109,0.00003869459,0.00001553497,0.000001612019,0.0000721891,0.9498535,0.0002019,0.00008440575,0.005803966,0.04327215],"study_design_scores_gemma":[0.0005534493,0.00002042332,0.00477817,0.00007421068,0.000006166125,7.501627e-7,0.00002478252,0.9918433,0.0006482499,0.0000311365,0.00180019,0.0002191116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1955448,0.000608217,0.7746497,0.00005022356,0.0006580211,0.01910338,0.00000612406,0.0005234447,0.00885601],"genre_scores_gemma":[0.9867342,0.000033027,0.005620942,0.00007727418,0.0001543467,0.00679144,0.00002041187,0.00004413709,0.0005242105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7911894,"threshold_uncertainty_score":0.6438981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442840137386437,"score_gpt":0.2350434613915462,"score_spread":0.2206150600176818,"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."}}