{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001117867,0.0006571878,0.0006926014,0.0004230197,0.0004965807,0.0007490749,0.001029105,0.0007772553,0.002346345],"category_scores_gemma":[0.003269674,0.000279196,0.0004315762,0.0005954641,0.0005589433,0.0009550892,0.0009140622,0.00115769,0.0008637168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005202006,"about_ca_system_score_gemma":0.001292601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00538042,"about_ca_topic_score_gemma":0.004107336,"domain_scores_codex":[0.9992693,0.0002038015,0.00004349907,0.0001429763,0.0002665966,0.0000738561],"domain_scores_gemma":[0.9990717,0.0004441164,0.0001013899,0.00007797133,0.0002748908,0.00002989869],"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":[0.0002461386,0.00006028177,0.001072743,0.000109714,0.00005296102,0.0001724326,0.0001796572,0.6150199,0.01329067,0.01254709,0.002414489,0.3548339],"study_design_scores_gemma":[0.000005178868,0.0000237387,0.00009447885,0.000004007697,0.00000485595,0.00002907386,0.000005339569,0.9968732,0.00162595,0.0009342285,0.0003939141,0.000006102716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006020656,0.000189751,0.9926428,0.00006216741,0.00003097238,0.00002178742,0.00001131803,0.0003260774,0.0006944251],"genre_scores_gemma":[0.462913,0.0005431204,0.529428,0.0001749903,0.0001023559,0.000196394,0.0002023321,0.0001456212,0.006294257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00538042,"threshold_uncertainty_score":0.0106982,"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."}}