{"id":"W2170007692","doi":"10.1109/twc.2006.1633352","title":"A simple remedy for the exaggerated extrinsic information produced by the SOVA algorithm","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"University of Arizona","keywords":"Additive white Gaussian noise; Algorithm; Computer science; Viterbi algorithm; Fading; Decoding methods; Viterbi decoder; Simple (philosophy); Soft output Viterbi algorithm; Convolutional code; White noise; Telecommunications; Sequential decoding; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003164568,0.0002608062,0.0001959154,0.0001369492,0.001470367,0.0001413194,0.001735554,0.0001195172,0.00001687184],"category_scores_gemma":[0.00001334214,0.0001983354,0.0001332868,0.0006946718,0.0002635691,0.0005908967,0.00001194727,0.0006163047,0.00002914778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001745692,"about_ca_system_score_gemma":0.00004742064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000176995,"about_ca_topic_score_gemma":0.0002076684,"domain_scores_codex":[0.9986113,0.000127463,0.0005830161,0.0001521946,0.0002222864,0.0003037844],"domain_scores_gemma":[0.995093,0.001179515,0.0001353993,0.003282147,0.0002671682,0.00004276337],"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":[0.00002629383,0.0003661341,0.000004159594,0.00004334829,0.0001663527,6.956938e-8,0.0005110364,0.1198046,0.01764615,0.004342525,0.02458286,0.8325065],"study_design_scores_gemma":[0.000560885,0.00005040085,0.00006385069,0.00004700057,0.00008731305,0.000006856707,0.0003910905,0.6842484,0.1697795,0.0009354508,0.1433787,0.0004505086],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001270626,0.0007319124,0.9917907,0.002575852,0.0001576938,0.00158872,0.0002975316,0.001223121,0.0003638685],"genre_scores_gemma":[0.9836184,0.001918518,0.01031511,0.000196799,0.00003113818,0.003534098,0.0001828861,0.00006391472,0.0001391038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9823478,"threshold_uncertainty_score":0.9998296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01736417242747467,"score_gpt":0.2552053535069252,"score_spread":0.2378411810794506,"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."}}