{"id":"W2019405212","doi":"10.1109/twc.2013.011713.120914","title":"Multi-Layer Iterative LDPC Decoding for Broadband Wireless Access Networks: A Recursive Shortening Algorithm","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Decoding methods; Algorithm; Low-density parity-check code; Wireless; Wireless broadband; Theoretical computer science; Wireless network; 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":[0.0004664037,0.0005367763,0.0003976985,0.0005381038,0.0004376958,0.0004473615,0.000706056,0.0006312973,0.001027172],"category_scores_gemma":[0.001871983,0.0002243819,0.0003410648,0.0005920113,0.0005220158,0.0006481113,0.0006827998,0.0006554361,0.0006269269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006576277,"about_ca_system_score_gemma":0.001191424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002204758,"about_ca_topic_score_gemma":0.003089159,"domain_scores_codex":[0.9995176,0.0001697186,0.00002895847,0.00005941892,0.0001814581,0.00004287881],"domain_scores_gemma":[0.9993162,0.0002839855,0.00008261378,0.0001624254,0.0001345214,0.00002033873],"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":[0.0001760202,0.0000720333,0.0009668597,0.0001347766,0.00003642597,0.000206504,0.0003249132,0.4517947,0.07861138,0.073043,0.001755793,0.3928776],"study_design_scores_gemma":[0.00001309404,0.00004599297,0.0001280724,0.00001360768,0.0000101997,0.0001223269,0.000009646132,0.9736974,0.01796368,0.005219319,0.002761079,0.00001558448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01142613,0.000199545,0.9863998,0.00007706846,0.00001336934,0.00003272671,0.00001797039,0.0003641364,0.001469264],"genre_scores_gemma":[0.2079594,0.0003384058,0.7883853,0.00007180492,0.00002524181,0.0001094443,0.00009768709,0.00007705821,0.002935752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002204758,"threshold_uncertainty_score":0.004771471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.065181307738267,"score_gpt":0.336237043108389,"score_spread":0.271055735370122,"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."}}