{"id":"W2121051850","doi":"10.1109/glocom.1999.831741","title":"Symbol-MAP-based trellis vector quantization","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Viterbi algorithm; Algorithm; Decoding methods; Sequential decoding; Iterative Viterbi decoding; Soft output Viterbi algorithm; Computer science; Trellis quantization; Vector quantization; Encoder; Trellis (graph); Convolutional code; Viterbi decoder; List decoding; Soft-decision decoder; Concatenated error correction code; Artificial intelligence; Image compression; Block code","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":[0.0006630297,0.0003959074,0.0006499428,0.000657753,0.0003409718,0.0009139534,0.001117117,0.0006851256,0.002360948],"category_scores_gemma":[0.003781493,0.0002094265,0.0003590168,0.001145405,0.0006343122,0.001590331,0.0006738802,0.001058546,0.001147326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007852206,"about_ca_system_score_gemma":0.0008860712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001894325,"about_ca_topic_score_gemma":0.002407992,"domain_scores_codex":[0.9991654,0.0001568315,0.00005691063,0.00009427285,0.0004616244,0.00006487131],"domain_scores_gemma":[0.9987716,0.0004723352,0.0000942945,0.0002127038,0.0004205887,0.00002850065],"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":[0.0002191512,0.00005523626,0.000536388,0.0002018255,0.00005444409,0.000107898,0.0001525836,0.3729073,0.02518168,0.1878656,0.005066365,0.4076516],"study_design_scores_gemma":[0.00002203851,0.00006173217,0.0001505378,0.00001486924,0.00001118683,0.0001150057,0.00001021232,0.9443183,0.01993514,0.03081303,0.004523272,0.00002459828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003650986,0.0002962854,0.9937512,0.0000889997,0.00006414903,0.00003113995,0.00007595166,0.0003823723,0.001658886],"genre_scores_gemma":[0.2822306,0.0008116653,0.7093106,0.0002064429,0.0001972012,0.0001794117,0.0004972453,0.0001718755,0.006394906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002360948,"threshold_uncertainty_score":0.007898152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515988019848158,"score_gpt":0.2634638770328365,"score_spread":0.2483039968343549,"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."}}