{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024529,0.0001891017,0.0001863665,0.0007788083,0.000240245,0.00003308645,0.0003643653,0.0002773152,0.00001476461],"category_scores_gemma":[0.00004342456,0.0002187302,0.00003404926,0.001686491,0.00009822214,0.000143515,0.00002172174,0.0006070659,0.00001566787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001653153,"about_ca_system_score_gemma":0.00001361436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000142708,"about_ca_topic_score_gemma":0.00004945134,"domain_scores_codex":[0.9989329,0.00005501478,0.0003917862,0.0002217817,0.0001039412,0.0002945689],"domain_scores_gemma":[0.9988908,0.0001545287,0.00005823492,0.0008413269,0.00002895939,0.00002610865],"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":[0.000004389613,0.00003331861,0.0001340434,0.0000106743,0.00002851742,8.220119e-7,0.0001128378,0.9559128,0.001580377,0.0003657091,0.0003387776,0.04147777],"study_design_scores_gemma":[0.000765947,0.00004981386,0.000182983,0.0001664363,0.00003276438,0.000003445753,0.003964407,0.9452158,0.02146789,0.0002450503,0.0274417,0.000463774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03123301,0.000500214,0.9589103,0.002445501,0.0001028371,0.0005803769,0.000006815582,0.005969426,0.0002514848],"genre_scores_gemma":[0.9858993,0.001679445,0.0117766,0.000008711362,0.000008542079,0.0003700709,0.00006420232,0.00004929514,0.0001438613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9546663,"threshold_uncertainty_score":0.8919557,"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."}}