{"id":"W2078337120","doi":"10.1109/tip.2006.888332","title":"Wiener Filter-Based Error Resilient Time-Domain Lapped Transform","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Lapped transform; Wiener filter; Computer science; Wiener deconvolution; Artificial intelligence; Computer vision; Mathematics; Algorithm; Discrete cosine transform; Speech recognition; Transform coding; Image (mathematics); Deconvolution","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":[0.0005461492,0.0003127479,0.0002513439,0.0004266851,0.0005247127,0.0002267473,0.0009301633,0.000120345,0.0000859343],"category_scores_gemma":[0.000007424825,0.0002905756,0.0001300035,0.0008161545,0.0001459618,0.001776392,0.000005500044,0.0004251498,0.00009237374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00013863,"about_ca_system_score_gemma":0.0001435632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006818733,"about_ca_topic_score_gemma":0.00001153935,"domain_scores_codex":[0.997632,0.00005747934,0.0004828792,0.0006964642,0.0005423898,0.0005887621],"domain_scores_gemma":[0.9985904,0.0001338735,0.0001405037,0.0007814423,0.000153405,0.000200354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001670187,0.0005517281,0.000001055726,0.0000960664,0.00001161169,0.00006852396,0.0005083954,0.001902251,0.2459546,0.00007396792,0.000597936,0.7500668],"study_design_scores_gemma":[0.0006727373,0.0001486739,0.0000135533,0.0002311736,0.00001195645,0.0000229433,0.00003746419,0.07369346,0.9193866,0.001731019,0.003625985,0.000424388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001017101,0.00005171203,0.9945967,0.0007484014,0.0001731289,0.0003544643,0.00002428375,0.001266331,0.001767924],"genre_scores_gemma":[0.3685321,0.000003261157,0.630392,0.0005227923,0.00002795081,0.00005568063,0.000004424801,0.00003670386,0.0004251349],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7496424,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536728177222463,"score_gpt":0.2899996638482461,"score_spread":0.2746323820760215,"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."}}