{"id":"W2168896875","doi":"10.1109/milcom.1993.408605","title":"Reduced state Viterbi receivers for digital mobile communications","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Viterbi algorithm; Computer science; Viterbi decoder; Iterative Viterbi decoding; Soft output Viterbi algorithm; Time division multiple access; Context (archaeology); Algorithm; Digital radio; Electronic engineering; Telecommunications; Decoding methods; Sequential decoding; Engineering","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":[0.0006469452,0.0003207906,0.0003651469,0.0005385692,0.0002689601,0.00102934,0.0009129299,0.0009939498,0.00567427],"category_scores_gemma":[0.003021905,0.0003140194,0.0002543052,0.0007611412,0.0003634974,0.001066282,0.0004383288,0.001255852,0.003198753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000833615,"about_ca_system_score_gemma":0.0007186189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001628064,"about_ca_topic_score_gemma":0.002416607,"domain_scores_codex":[0.9995162,0.0001517102,0.00001956639,0.00005721402,0.0002190071,0.0000363049],"domain_scores_gemma":[0.99937,0.0002822388,0.00004406473,0.000114276,0.0001783621,0.00001108098],"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":[0.0002471977,0.000110302,0.0005365165,0.0003528679,0.00006701802,0.0001539218,0.0002586143,0.1486314,0.03951987,0.3908537,0.0180257,0.401243],"study_design_scores_gemma":[0.00005407954,0.0001695669,0.0002807646,0.00008112729,0.00003637567,0.0002140996,0.00002667132,0.7861464,0.02899303,0.09676903,0.0871815,0.00004733314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00567203,0.001944866,0.9809583,0.0006341549,0.0001872556,0.00004399148,0.00006846911,0.001123916,0.00936691],"genre_scores_gemma":[0.2491381,0.004491966,0.6927652,0.0007849814,0.0005181337,0.0002436834,0.0006351352,0.0002286878,0.0511941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00567427,"threshold_uncertainty_score":0.01898235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03271166630317981,"score_gpt":0.2524780692121979,"score_spread":0.2197664029090181,"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."}}