{"id":"W1998457119","doi":"10.1109/icc.2004.1312548","title":"Fast length-constrained MAP decoding of variable length coded Markov sequences over noisy channel","year":2004,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Decoding methods; Sequential decoding; List decoding; Algorithm; Markov chain; Directed acyclic graph; Sequence (biology); Mathematics; Parameterized complexity; Markov process; Computer science; Heuristic; Constraint (computer-aided design); Mathematical optimization; Concatenated error correction code; 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":[0.0007706845,0.0005671186,0.0008165844,0.0004401198,0.0004062411,0.0006260934,0.0005927842,0.000900859,0.001092111],"category_scores_gemma":[0.005128653,0.0003798228,0.0003476673,0.0009791198,0.0007304752,0.001283627,0.0008064267,0.0007116066,0.0002292071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006586199,"about_ca_system_score_gemma":0.001713015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003936198,"about_ca_topic_score_gemma":0.003053073,"domain_scores_codex":[0.999492,0.0001988569,0.00002264812,0.00007591184,0.0001435574,0.00006694681],"domain_scores_gemma":[0.9971921,0.002262166,0.0002006453,0.0001229197,0.0001854864,0.00003678539],"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":[0.0002067514,0.0000177425,0.0002907394,0.00009463071,0.00002435269,0.0001040041,0.00007519083,0.9181324,0.004219966,0.02710637,0.0006918902,0.04903595],"study_design_scores_gemma":[0.000009804792,0.00001230344,0.00004308365,0.000004075946,0.000003293805,0.00002082867,0.000007651493,0.9892411,0.001957019,0.008541675,0.0001554704,0.000003664727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03960224,0.0001759239,0.9585976,0.0001776183,0.00001367284,0.00002525952,0.0000671173,0.0002259263,0.001114729],"genre_scores_gemma":[0.6900805,0.0006799002,0.3049578,0.00006980955,0.00005229641,0.0001272403,0.0003108329,0.00009559185,0.003626041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003936198,"threshold_uncertainty_score":0.007826567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01511389599807331,"score_gpt":0.2518160644528165,"score_spread":0.2367021684547432,"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."}}