{"id":"W2127731639","doi":"10.1109/lcomm.2006.1613728","title":"Code detection in turbo source coding","year":2006,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Turbo code; Constant-weight code; Lossless compression; Systematic code; Concatenated error correction code; Variable-length code; Algorithm; Decoding methods; Code (set theory); Data compression; Low-density parity-check code; Turbo equalizer; Distributed source coding; Theoretical computer science; Block code; 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":[0.001573699,0.0007847157,0.0008823491,0.0008816209,0.0004609557,0.001104099,0.001008901,0.001949774,0.0006697055],"category_scores_gemma":[0.009826946,0.0004823989,0.0004845335,0.001276802,0.001851643,0.00192834,0.001062599,0.001046968,0.0003158031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009416426,"about_ca_system_score_gemma":0.000933497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002389378,"about_ca_topic_score_gemma":0.001129512,"domain_scores_codex":[0.9985729,0.0006441035,0.00003975853,0.0001302423,0.0005042407,0.0001086853],"domain_scores_gemma":[0.9964244,0.002553506,0.0002372904,0.000182659,0.0005332989,0.00006882166],"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":[0.0001568505,0.00002470035,0.0006865224,0.0001654115,0.00003064597,0.0003846582,0.0001298503,0.7511271,0.003436398,0.2114401,0.001641741,0.03077602],"study_design_scores_gemma":[0.000007922203,0.00001913616,0.00003701207,0.000009115088,0.000004236715,0.00004687597,0.000004531412,0.9725062,0.001079515,0.02595908,0.0003168185,0.000009535591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02227269,0.00119099,0.9727674,0.0003222455,0.0001097084,0.0000356934,0.00004589036,0.000159924,0.003095414],"genre_scores_gemma":[0.7940242,0.002433661,0.1960464,0.0002520032,0.0003517311,0.000186803,0.0001369166,0.00009815638,0.006470088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002389378,"threshold_uncertainty_score":0.008322597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01559574748167377,"score_gpt":0.2453168266344713,"score_spread":0.2297210791527975,"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."}}