{"id":"W4306403363","doi":"10.3390/electronics11203337","title":"The Efficient Design of Lossy P-LDPC Codes over AWGN Channels","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Low-density parity-check code; Additive white Gaussian noise; Lossy compression; Algorithm; Computer science; Turbo code; Robustness (evolution); Distortion (music); Forward error correction; Decoding methods; Channel (broadcasting); Electronic engineering; Telecommunications; Bandwidth (computing); Engineering","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":[0.0004268369,0.0005015788,0.0003593712,0.0004193168,0.0002498697,0.000486834,0.0004704688,0.0004412035,0.0004162645],"category_scores_gemma":[0.002100205,0.000261611,0.000169926,0.000521875,0.0005427744,0.0006141833,0.0004195395,0.0005081696,0.0002099203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005246744,"about_ca_system_score_gemma":0.0009817114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008522787,"about_ca_topic_score_gemma":0.001282537,"domain_scores_codex":[0.9995576,0.0001310682,0.00002234141,0.00005805244,0.0001942555,0.00003680724],"domain_scores_gemma":[0.9993062,0.0003102804,0.0001447226,0.00007878849,0.0001386965,0.00002134604],"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":[0.0001264712,0.00003186708,0.0006356123,0.0002624713,0.00004022779,0.0001798238,0.0001175858,0.7993618,0.08005738,0.04351255,0.0006211792,0.07505295],"study_design_scores_gemma":[0.00001694886,0.0000685798,0.0001730619,0.00001874878,0.00001070693,0.0001316675,0.00001174965,0.9592601,0.03156443,0.007056633,0.001674531,0.00001296423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03319836,0.0004316092,0.9642774,0.00009286762,0.00001561139,0.00005844188,0.00004940363,0.0001547698,0.001721544],"genre_scores_gemma":[0.6697776,0.001238976,0.3267496,0.000065562,0.00003039063,0.0001539173,0.0001156727,0.00006081759,0.001807379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008522787,"threshold_uncertainty_score":0.00380677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503284483818865,"score_gpt":0.2528993398867009,"score_spread":0.2378664950485122,"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."}}