{"id":"W3196762896","doi":"10.1109/tgcn.2021.3099580","title":"Editorial Energy Efficiency of Machine-Learning-Based Designs for Future Wireless Systems and Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Wireless; MIMO; Context (archaeology); Wireless network; Slicing; Transmission (telecommunications); The Internet; Efficient energy use; Computer network; Telecommunications; Resource (disambiguation); Engineering; Electrical engineering; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002555371,0.0008364051,0.0006221905,0.0008864437,0.0006762707,0.002149276,0.001196871,0.00188637,0.014607],"category_scores_gemma":[0.01119259,0.0002845174,0.0003836802,0.0006799783,0.001154078,0.002360134,0.0004895212,0.003410282,0.004817828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006831035,"about_ca_system_score_gemma":0.0006922493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004585747,"about_ca_topic_score_gemma":0.0007261536,"domain_scores_codex":[0.9987636,0.0002425668,0.00007057952,0.000152171,0.0007054274,0.00006565272],"domain_scores_gemma":[0.9918142,0.004073892,0.0002361139,0.000356486,0.003163002,0.0003563348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008862564,0.00003310653,0.0001200686,0.0004264368,0.00003141933,0.0000870954,0.00003059594,0.003250845,0.0006245189,0.04468436,0.8468623,0.1037608],"study_design_scores_gemma":[0.00002555778,0.0001081249,0.0001702634,0.0002762297,0.00002748243,0.0002017912,0.00002869627,0.01017382,0.0009670171,0.0408422,0.9471539,0.00002488956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00268932,0.08038609,0.08023979,0.1048208,0.6643297,0.0001093106,0.0006479815,0.000768966,0.06600806],"genre_scores_gemma":[0.07106593,0.06853969,0.02556097,0.01754628,0.7073279,0.0001210938,0.0004812698,0.0006029503,0.108754],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.014607,"threshold_uncertainty_score":0.04886532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03116252898141737,"score_gpt":0.2489336536853841,"score_spread":0.2177711247039668,"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."}}