{"id":"W4324149179","doi":"10.1002/ett.4763","title":"Resource allocation and user assignment schemes in cellular supported industrial IoT networks","year":2023,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Toronto Metropolitan University; Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subcarrier; Computer science; Resource allocation; Throughput; Telecommunications link; Computer network; Interference (communication); Cellular network; Internet of Things; Resource management (computing); Genetic algorithm; Mathematical optimization; Wireless; Orthogonal frequency-division multiplexing; Telecommunications; Mathematics; 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":[0.0005042511,0.0004484006,0.0004044105,0.0004205134,0.0005629138,0.0005218466,0.0008076698,0.0004657897,0.0009099915],"category_scores_gemma":[0.001438981,0.0001585524,0.0001947539,0.0007194257,0.0005103454,0.0004423336,0.000647589,0.0003126835,0.0001382311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000701435,"about_ca_system_score_gemma":0.0005612886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003934947,"about_ca_topic_score_gemma":0.004041287,"domain_scores_codex":[0.999426,0.0002358096,0.00001520521,0.00006051577,0.00009451839,0.0001679305],"domain_scores_gemma":[0.9994934,0.0002378817,0.00007624758,0.00004493826,0.0001049764,0.00004257584],"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":[0.0002024814,0.0001088785,0.001560068,0.00003773729,0.0000324848,0.0001684745,0.0001048532,0.9209072,0.005128712,0.01036987,0.001146581,0.06023274],"study_design_scores_gemma":[0.000006319204,0.00003161209,0.0001884463,0.000002561449,0.000005202022,0.00002771565,0.00002971052,0.9970203,0.0005620114,0.001875372,0.0002470109,0.000003769909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3191744,0.0006400486,0.6718028,0.0002416372,0.00005670558,0.0001137171,0.00005316911,0.0002013501,0.007716158],"genre_scores_gemma":[0.9865317,0.00007050375,0.01272534,0.00002499519,0.000007066587,0.0000331165,0.00001321143,0.000003767241,0.0005902515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003934947,"threshold_uncertainty_score":0.007824063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0224837884830716,"score_gpt":0.2424525119483489,"score_spread":0.2199687234652773,"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."}}