{"id":"W1730211723","doi":"10.1002/wcm.2321","title":"Modified linear prediction algorithm for narrowband interference suppression in universal mobile telecommunication system","year":2012,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Narrowband; Telecommunications; Interference (communication); Algorithm; Linear prediction; Mobile telephony; Speech recognition; Mobile radio","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001626637,0.0002522029,0.0003563755,0.000342521,0.000889799,0.0001778011,0.003321392,0.0001866413,0.00000169741],"category_scores_gemma":[0.00002565735,0.0002653173,0.00007778261,0.0007312773,0.0002192222,0.0009659248,0.003054927,0.0006127187,0.000004829947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002852907,"about_ca_system_score_gemma":0.00009148518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000111939,"about_ca_topic_score_gemma":0.00002013453,"domain_scores_codex":[0.9972952,0.0007573355,0.0006934769,0.0004115158,0.0002404037,0.0006020499],"domain_scores_gemma":[0.9947048,0.001186326,0.0002934313,0.003315134,0.0002994929,0.0002008193],"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":[0.00001785989,0.0005746232,0.002017324,0.0001127585,0.00003547414,4.509136e-7,0.006145397,0.008254815,0.001090634,0.02111358,0.0001172667,0.9605198],"study_design_scores_gemma":[0.0007756271,0.0001213002,0.0008748177,0.0003227032,0.00000820304,0.00001861721,0.00141195,0.9921718,0.0003064897,0.00003901434,0.003708359,0.000241096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.117011,0.005003541,0.8750941,0.0001695785,0.0002354426,0.00159238,0.00002265197,0.0003605127,0.0005108176],"genre_scores_gemma":[0.8739442,0.001365341,0.12377,0.0000191061,0.00007142416,0.0006582257,0.000114495,0.00002734796,0.00002978294],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.983917,"threshold_uncertainty_score":0.9999799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03395465143011017,"score_gpt":0.305464741321971,"score_spread":0.2715100898918608,"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."}}