{"id":"W3213976927","doi":"10.1109/istc49272.2021.9594229","title":"Multi Coding Rates Nested Recursive Convolutional Doubly-Orthogonal Codes","year":2021,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Convolutional code; Encoder; Coding (social sciences); Tanner graph; Decoding methods; Block code; Discrete mathematics; Serial concatenated convolutional codes; Concatenated error correction code; Linear code; Mathematics; Algorithm; Computer science; Code rate; Combinatorics; Theoretical computer science; Statistics","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":[0.0003901097,0.0003315696,0.0003360624,0.0005819785,0.0003077392,0.0005487321,0.0006892292,0.0003402854,0.001170317],"category_scores_gemma":[0.002124181,0.0001309943,0.0002964767,0.0004163103,0.0006732062,0.0005719829,0.0007797241,0.0005399755,0.0003936028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005776344,"about_ca_system_score_gemma":0.0005955901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001661227,"about_ca_topic_score_gemma":0.002172032,"domain_scores_codex":[0.9994704,0.00007679255,0.00002891267,0.00009003501,0.0002228252,0.0001109993],"domain_scores_gemma":[0.9985386,0.0004605675,0.0002041956,0.000366927,0.0003578851,0.00007187361],"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":[0.0004522891,0.00008554792,0.003815562,0.0002278778,0.00005318598,0.0006119081,0.0004047581,0.1819832,0.1241602,0.4739984,0.002275666,0.2119313],"study_design_scores_gemma":[0.00004075513,0.0002316401,0.001912624,0.00005721342,0.00004290542,0.000954994,0.00006018957,0.8341591,0.06757956,0.07702222,0.01785783,0.00008091271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2306738,0.0007518182,0.7499171,0.0001259772,0.0000705917,0.00007216918,0.000247119,0.0006280703,0.01751334],"genre_scores_gemma":[0.8756909,0.000426568,0.1181465,0.0001053749,0.00003634328,0.00007689677,0.0002938214,0.00007350757,0.005150209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001661227,"threshold_uncertainty_score":0.004190981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04202779564541497,"score_gpt":0.3089258124863023,"score_spread":0.2668980168408873,"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."}}