{"id":"W2114064510","doi":"10.1109/pacrim.2011.6033011","title":"Multicarrier error correction using T-codes","year":2011,"lang":"en","type":"article","venue":"","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Code word; Computer science; Code (set theory); Algorithm; Error detection and correction; Transmission (telecommunications); Forward error correction; Variable (mathematics); Domain (mathematical analysis); Decoding methods; Discrete mathematics; Theoretical computer science; Mathematics; Telecommunications; Programming language","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":[0.0008786429,0.000754926,0.0006917861,0.0009572119,0.0007229287,0.00116622,0.00116499,0.001142313,0.001394155],"category_scores_gemma":[0.004393382,0.0002080929,0.0007395116,0.001653109,0.001170409,0.001768412,0.001346536,0.0008775151,0.0008395435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079127,"about_ca_system_score_gemma":0.001648523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003680712,"about_ca_topic_score_gemma":0.002423918,"domain_scores_codex":[0.9983287,0.0004938671,0.00009723513,0.0002476341,0.0005959817,0.0002364672],"domain_scores_gemma":[0.9978983,0.0007691303,0.000312631,0.0004346448,0.0005294364,0.00005570919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005893566,0.0000876089,0.001297652,0.0007229383,0.00014577,0.0007746101,0.0004760634,0.3827136,0.03954271,0.3919691,0.003064382,0.1786162],"study_design_scores_gemma":[0.00007947067,0.0002219293,0.0002753324,0.0001424501,0.00005416789,0.0006572658,0.00007327349,0.8773137,0.0305194,0.07575095,0.01483076,0.00008128586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02365366,0.00206564,0.9655179,0.000364868,0.0002077409,0.00008787258,0.00007525565,0.0004517458,0.007575319],"genre_scores_gemma":[0.665284,0.004013262,0.3219898,0.0003230774,0.0002497453,0.0001990709,0.0001606451,0.0001027678,0.007677655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003680712,"threshold_uncertainty_score":0.007829666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07120405676965574,"score_gpt":0.2644219022569285,"score_spread":0.1932178454872728,"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."}}