{"id":"W4411202934","doi":"10.1109/icjece.2025.3568042","title":"A Spectral and Energy Efficient Noise Variance and SNR Estimator for DMH OFDM-IM Systems","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Variance (accounting); Energy (signal processing); Noise (video); Physics; Mathematics; Statistics; Statistical physics; Algorithm; Computer science; Economics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007071914,0.0001154274,0.0002092749,0.0002597944,0.00006280178,0.00007123406,0.0001055177,0.00005251593,3.081452e-7],"category_scores_gemma":[0.00002221084,0.0001166374,0.00002436108,0.0001619248,0.00002148096,0.00005708887,0.0000129414,0.0001500475,3.216425e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008273371,"about_ca_system_score_gemma":0.00005695763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000674418,"about_ca_topic_score_gemma":0.00002799889,"domain_scores_codex":[0.9994387,0.000007363195,0.0002173195,0.00008654188,0.00003945054,0.0002106308],"domain_scores_gemma":[0.9994692,0.0001291393,0.00002942928,0.00008558805,0.0000484324,0.0002382725],"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":[0.00001281495,0.00001252741,0.0003627657,0.0003308336,0.0001410498,0.00003762415,0.0001164123,0.7794873,0.001599972,0.1349352,0.0008712401,0.08209225],"study_design_scores_gemma":[0.0002123199,0.00005841324,0.000852745,0.0001464835,0.00001304477,0.0001103101,0.000002329195,0.9903375,0.0003640764,0.0001820664,0.007604085,0.0001166565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03040085,0.01162253,0.9575705,0.00008165895,0.0001707203,0.00006029917,0.000003111419,0.00004602283,0.00004433359],"genre_scores_gemma":[0.9766992,0.0002479811,0.02290647,0.00002903037,0.00007654752,0.000009017166,5.974862e-7,0.00001511525,0.00001598143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9462984,"threshold_uncertainty_score":0.4756337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003513064950186649,"score_gpt":0.1811979384630675,"score_spread":0.1776848735128809,"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."}}