{"id":"W2153450807","doi":"10.1109/dcc.2003.1194074","title":"Soft-decoding based vector quantization for hidden-Markov channels","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fading; Decoding methods; Rayleigh fading; Algorithm; Computer science; Vector quantization; Hidden Markov model; Markov process; Channel (broadcasting); Markov model; Minimum mean square error; Channel state information; Markov chain; Speech recognition; Wireless; Mathematics; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007179815,0.0005093912,0.0007283603,0.0003375039,0.0003019126,0.0008017619,0.0007200388,0.0005648034,0.00493428],"category_scores_gemma":[0.004059926,0.0001889891,0.0002169717,0.0007549742,0.0006383732,0.0009204329,0.0005443622,0.0008748894,0.001091801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006236272,"about_ca_system_score_gemma":0.0007822802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002660825,"about_ca_topic_score_gemma":0.002877158,"domain_scores_codex":[0.9993643,0.00020376,0.0000414938,0.0000795446,0.0002608851,0.0000500588],"domain_scores_gemma":[0.9983162,0.001101165,0.00009493548,0.0001928977,0.0002653077,0.00002956411],"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":[0.0002621548,0.0000453897,0.0004674564,0.0003357193,0.00004036763,0.0001652206,0.0001240894,0.5455135,0.007522297,0.1070483,0.007773533,0.3307019],"study_design_scores_gemma":[0.000008691451,0.00002815423,0.00005348603,0.00001455254,0.000005252973,0.00003580838,0.000007309965,0.9823909,0.002103248,0.01379412,0.001549066,0.000009375624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009783978,0.001350283,0.9851893,0.0002757714,0.000201011,0.0000517001,0.0001343343,0.0005034566,0.002510198],"genre_scores_gemma":[0.665432,0.002415306,0.3209284,0.000231464,0.0003020908,0.0001731507,0.000587402,0.0001140397,0.009816141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00493428,"threshold_uncertainty_score":0.01650679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082896390473764,"score_gpt":0.2968498563786044,"score_spread":0.2660208924738667,"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."}}