{"id":"W1546736863","doi":"10.1109/iscas.2002.1010408","title":"An adaptive Viterbi algorithm based on strongly connected trellis decoding","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Viterbi decoder; Iterative Viterbi decoding; Soft output Viterbi algorithm; Viterbi algorithm; Sequential decoding; Computer science; Convolutional code; Trellis (graph); Algorithm; Decoding methods; 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":[0.0006159756,0.0004944878,0.0005258682,0.0007616448,0.0004817891,0.0008029171,0.001399101,0.001039435,0.002314342],"category_scores_gemma":[0.002296813,0.0003807429,0.0004295795,0.0008485133,0.0005175403,0.0007822753,0.000592728,0.001248439,0.001185928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008117236,"about_ca_system_score_gemma":0.001782476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006338513,"about_ca_topic_score_gemma":0.006310294,"domain_scores_codex":[0.9994152,0.0001257819,0.00003812879,0.0001108983,0.0002375233,0.00007250188],"domain_scores_gemma":[0.9991676,0.0003484408,0.00006346456,0.00009975792,0.0002922512,0.00002853637],"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":[0.0003654872,0.0001330035,0.001116767,0.0001550367,0.0001077757,0.0001522588,0.0001508202,0.4166747,0.06882805,0.04946556,0.00459557,0.4582548],"study_design_scores_gemma":[0.00004526759,0.000101853,0.0002312136,0.00001291552,0.00002364698,0.000122644,0.00000816949,0.9691057,0.01930636,0.007058143,0.003957629,0.00002646289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007602789,0.0001620547,0.9889728,0.00009389104,0.00004711068,0.00006564421,0.00004408952,0.0008110495,0.002200577],"genre_scores_gemma":[0.2637224,0.0003496428,0.7269854,0.0001774579,0.00007712415,0.0002207981,0.0003462065,0.0001311826,0.007989749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006338513,"threshold_uncertainty_score":0.01260322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436156186638068,"score_gpt":0.2503222294678282,"score_spread":0.2359606676014475,"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."}}