{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001489356,0.000121346,0.0001377264,0.00007355875,0.0005131124,0.00006655633,0.001536739,0.0000287423,0.0000119426],"category_scores_gemma":[0.00008589827,0.0001004758,0.00006949971,0.0005916994,0.0000495352,0.00005741452,0.000574232,0.0003829992,0.000004091452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002583819,"about_ca_system_score_gemma":0.0002717125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001102368,"about_ca_topic_score_gemma":0.000005236305,"domain_scores_codex":[0.998263,0.0002877561,0.0002198014,0.0002860347,0.0004767283,0.0004666923],"domain_scores_gemma":[0.9986067,0.0004331083,0.0001639802,0.0006968703,0.00006650743,0.0000327656],"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":[0.0001419056,0.0006412449,0.0002108463,0.00003143905,0.0001449416,0.00003156501,0.006143461,0.2642657,0.05061075,0.5545017,0.03109455,0.09218198],"study_design_scores_gemma":[0.00026051,0.001196276,0.00006146864,0.0000122163,0.0000121608,0.00005897038,0.00006767295,0.7858029,0.1321971,0.02326333,0.0567243,0.0003430757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02352539,0.005509057,0.9684541,0.0006481781,0.0005984885,0.0003860469,0.000001695165,0.0004310748,0.0004459727],"genre_scores_gemma":[0.9900212,0.0001105554,0.009159838,0.0001576537,0.00003216889,0.00009630198,7.812775e-7,0.00001802225,0.0004035386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9664958,"threshold_uncertainty_score":0.4097283,"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."}}