{"id":"W2243444140","doi":"","title":"Design of LDPC codes for BP algorithm with channel estimation error","year":2006,"lang":"en","type":"article","venue":"Turbo Codes&Related Topics; 6th International ITG-Conference on Source and Channel Coding (TURBOCODING), 2006 4th International Symposium on","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Low-density parity-check code; Algorithm; Channel (broadcasting); Computer science; Error floor; Error detection and correction; Decoding methods; Telecommunications","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":[0.0004655613,0.00073006,0.0005835996,0.0007589607,0.0007121934,0.0008499468,0.0006981818,0.001032391,0.001777057],"category_scores_gemma":[0.002330439,0.0004069222,0.0003314568,0.0008267967,0.0004417087,0.0005091065,0.0005758181,0.0007716645,0.0007888761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008767053,"about_ca_system_score_gemma":0.002223551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003113583,"about_ca_topic_score_gemma":0.003546718,"domain_scores_codex":[0.9994859,0.0001400129,0.00003048593,0.00009580971,0.0001886239,0.00005921237],"domain_scores_gemma":[0.9990049,0.0003252963,0.00009259977,0.0000979585,0.0004350999,0.00004403137],"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":[0.0005135812,0.0001213888,0.002232192,0.0006378401,0.0001449442,0.0004219786,0.0004158631,0.4075597,0.1012316,0.09999429,0.00457732,0.3821494],"study_design_scores_gemma":[0.00007941716,0.0001833418,0.0006217859,0.00009126148,0.00006345271,0.0004270541,0.00003385997,0.9225786,0.04558517,0.02072383,0.00955814,0.00005405409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01194376,0.0004757347,0.983376,0.0001929693,0.00006148024,0.00008567706,0.00008973211,0.0004034948,0.003371274],"genre_scores_gemma":[0.3738311,0.0009245701,0.619765,0.0002067452,0.00009808709,0.0003988596,0.0002966769,0.0001135656,0.004365493],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003113583,"threshold_uncertainty_score":0.006361008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02983997712224189,"score_gpt":0.2731340310799741,"score_spread":0.2432940539577322,"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."}}