{"id":"W2093289920","doi":"10.1109/mmsp.2005.248559","title":"Rate-distortion Optimization for MP3 Audio Coding with Complete Decoder Compatibility","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Huffman coding; Quantization (signal processing); Computer science; Codec; Algorithm; Speech coding; Coding (social sciences); Mathematical optimization; Mathematics; Speech recognition; Data compression; Telecommunications","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":[0.0002697912,0.000129352,0.0001642378,0.00008512617,0.0002625015,0.0001360312,0.0005258541,0.00005634531,0.00003224777],"category_scores_gemma":[0.00005666912,0.00009708896,0.00004297314,0.0002198415,0.00004964274,0.0004741494,0.0001266741,0.00007764081,0.00001183889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007928698,"about_ca_system_score_gemma":0.00003347719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000106883,"about_ca_topic_score_gemma":0.00003956847,"domain_scores_codex":[0.9989758,0.00003891423,0.0002270226,0.000389954,0.0001469159,0.0002214659],"domain_scores_gemma":[0.999046,0.0001271998,0.0001134862,0.000515556,0.0001510201,0.00004676209],"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":[0.0001090108,0.0002955318,0.002430941,0.00007160023,0.00005141974,0.000002021915,0.0006588422,0.6735661,0.00217698,0.08089147,0.01505198,0.2246941],"study_design_scores_gemma":[0.0004744046,0.0001084245,0.0007577435,0.00002505435,0.000005008162,0.000003596234,0.00004190357,0.9859389,0.009294389,0.0008480048,0.002326378,0.0001761987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01550804,0.00002584742,0.9803112,0.002092051,0.00009217429,0.0002630096,0.000002313798,0.000978716,0.00072664],"genre_scores_gemma":[0.6419821,0.000004616996,0.3576027,0.0002012226,0.00001968866,0.00003381448,0.000004950206,0.000005061208,0.0001457885],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6264741,"threshold_uncertainty_score":0.3959173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03544906572348912,"score_gpt":0.2578884560229678,"score_spread":0.2224393902994787,"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."}}