{"id":"W4242022488","doi":"10.1109/cjece.2016.2523347","title":"Table of contents","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Information retrieval; Computer science; Database","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":[0.00004371388,0.00006772804,0.0001440993,0.0001970133,0.00001160811,0.00001050586,0.00008492806,0.00003369856,0.00001466386],"category_scores_gemma":[0.00002404376,0.00005099268,0.00003189234,0.0001381219,0.00001414001,0.00008689893,0.000003420809,0.0000850367,0.000001207452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004645104,"about_ca_system_score_gemma":0.00004147084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003424761,"about_ca_topic_score_gemma":0.00001229199,"domain_scores_codex":[0.9995245,0.000003707705,0.0001947239,0.00004081864,0.00005692385,0.0001792793],"domain_scores_gemma":[0.9995456,0.00004476147,0.00002610225,0.00004540952,0.00005531307,0.0002827719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000338578,0.00004548929,0.02348676,0.0003259745,0.001044953,0.0004558803,0.0005099092,0.2023625,0.2733964,0.01528465,0.04081485,0.4422387],"study_design_scores_gemma":[0.004732274,0.001275292,0.1020637,0.001449787,0.0001311631,0.0038935,0.00001580863,0.4973882,0.1025637,0.001018573,0.2839431,0.001524918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6412187,0.003400287,0.3535751,0.0001530844,0.001315146,0.00004991813,0.000005801905,0.00003526013,0.0002467524],"genre_scores_gemma":[0.9984744,0.00005104012,0.00125238,0.000009246151,0.000178823,4.036483e-7,7.32723e-8,0.00001052796,0.0000231441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4407138,"threshold_uncertainty_score":0.2079421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005686757727533567,"score_gpt":0.1525525487511695,"score_spread":0.1468657910236359,"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."}}