{"id":"W2154204394","doi":"10.1109/dcc.2012.16","title":"Context Modeling and Correction of Quantization Errors in Prediction Loop","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Quantization (signal processing); Pulse-code modulation; Computer science; Robustness (evolution); Lossy compression; Algorithm; Mean squared prediction error; Coding (social sciences); Speech recognition; Artificial intelligence; Control theory (sociology); Mathematics; Statistics","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":[0.0001749729,0.00004670089,0.00007028858,0.0001129913,0.00002143495,0.000009100907,0.0001011138,0.00003460744,0.000003490253],"category_scores_gemma":[0.00004016076,0.00004231735,0.00000759271,0.0001779443,0.00001176745,0.001316939,0.00009376914,0.00004985109,9.999071e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001649133,"about_ca_system_score_gemma":0.000006381732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000818362,"about_ca_topic_score_gemma":0.00001420005,"domain_scores_codex":[0.9995102,0.00002912729,0.0001668738,0.0001146382,0.00008964942,0.00008947864],"domain_scores_gemma":[0.9996907,0.00002535396,0.00005251108,0.0001643676,0.00003851804,0.0000286071],"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":[0.00004014623,0.0003369213,0.1094572,0.00005358161,0.000008768708,5.952874e-7,0.002802499,0.04985697,0.03956264,0.08584145,0.001400999,0.7106383],"study_design_scores_gemma":[0.00008574597,0.00001943016,0.001495893,0.00003030772,7.52556e-7,0.00000247731,0.00007461262,0.9761112,0.02154901,0.0005121053,0.00007767084,0.00004075538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1126936,0.00008964523,0.8864478,0.00002077928,0.0002450946,0.00008126337,7.549171e-7,0.0001296375,0.0002913989],"genre_scores_gemma":[0.9538462,0.00003951641,0.04603234,0.00002608112,0.00000990925,0.000005828767,0.000003125123,0.000002729364,0.00003429373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9262543,"threshold_uncertainty_score":0.1725651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02997887052156489,"score_gpt":0.2856991958722552,"score_spread":0.2557203253506903,"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."}}