{"id":"W4410087475","doi":"10.1109/ccnc54725.2025.10975884","title":"A Semi-Supervised QPSO-TR PAPR Reduction Scheme for OTSM Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"PAPR reduction in OFDM","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Reduction (mathematics); Scheme (mathematics); Computer science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001215477,0.0001415732,0.0001683513,0.0001486175,0.00007313093,0.00005432614,0.0001179532,0.0001181364,0.0000553138],"category_scores_gemma":[0.00003399879,0.0001451651,0.00007557489,0.000278898,0.0000202356,0.0001446414,0.00001701143,0.0001044211,0.0000621531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001450464,"about_ca_system_score_gemma":0.00002597831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002765116,"about_ca_topic_score_gemma":0.000001186777,"domain_scores_codex":[0.9992094,0.00001246699,0.0002579329,0.0001929467,0.00009675175,0.0002304945],"domain_scores_gemma":[0.9995592,0.00002375846,0.00001668154,0.0002762804,0.00007231405,0.0000518007],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003156487,0.00005109652,0.0001184471,0.001411823,0.0003316294,0.000001012103,0.0002476225,0.06832796,0.5782626,0.0263736,0.3206456,0.004197062],"study_design_scores_gemma":[0.00143074,0.00003353,0.0001235,0.0003082074,0.00006568134,0.00003938247,0.001590866,0.525162,0.1328138,0.0006760607,0.3371925,0.0005638257],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4576722,0.005357114,0.2751416,0.001734029,0.02743015,0.003182553,0.00004180976,0.006505561,0.2229349],"genre_scores_gemma":[0.969741,0.00005531234,0.004338393,0.00003341605,0.0005252189,0.0004423555,0.00001526758,0.00004370304,0.02480527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5120688,"threshold_uncertainty_score":0.5919661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01190639913243168,"score_gpt":0.2408289421640672,"score_spread":0.2289225430316355,"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."}}