{"id":"W3117711547","doi":"10.1109/jiot.2020.3045831","title":"MAC for Machine-Type Communications in Industrial IoT—Part II: Scheduling and Numerical Results","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Scheduling (production processes); Quality of service; Computer network; Network packet; Distributed computing; Granularity","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":[0.002246528,0.0006006019,0.000670742,0.0008205081,0.0004927297,0.001024072,0.0009060925,0.001139654,0.003096778],"category_scores_gemma":[0.008274149,0.0002848689,0.0005147863,0.0007852981,0.0009463351,0.001606881,0.0009216088,0.001757114,0.0003726787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001658063,"about_ca_system_score_gemma":0.001424517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005610691,"about_ca_topic_score_gemma":0.004532428,"domain_scores_codex":[0.9994199,0.0002205028,0.00002738642,0.00005395441,0.0002041552,0.00007399343],"domain_scores_gemma":[0.9970626,0.001971155,0.0002228332,0.0001823669,0.0004906398,0.0000703692],"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":[0.00001996782,0.00004645417,0.0005201258,0.00008049765,0.00001159368,0.00003422967,0.00003018388,0.9383053,0.0009095774,0.04786023,0.001754192,0.01042758],"study_design_scores_gemma":[0.000001988568,0.0000045075,0.00003821242,0.000005398987,0.000001174496,0.000004929697,0.000005406685,0.9955733,0.000137843,0.003872947,0.0003524566,0.000001800975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01711863,0.002229252,0.9573599,0.001940355,0.0002680803,0.0001261415,0.00008562995,0.0002965322,0.0205755],"genre_scores_gemma":[0.6545801,0.002805549,0.3312097,0.0007420635,0.0004211417,0.0004546628,0.0001885543,0.0001874807,0.009410776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005610691,"threshold_uncertainty_score":0.01203018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08489517120588,"score_gpt":0.3050490466203007,"score_spread":0.2201538754144207,"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."}}