{"id":"W3017318545","doi":"10.3390/s20082300","title":"A New Construction of High Performance LDPC Matrices for Mobile Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Low-density parity-check code; Computer science; Additive white Gaussian noise; Computation; Algorithm; Decoding methods; Data flow diagram; Channel (broadcasting); Theoretical computer science; Computer engineering; Computer network","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":[0.00008776759,0.0000863012,0.0001449788,0.00004564994,0.00004651332,0.00002606237,0.0003481125,0.00005318748,0.000005448508],"category_scores_gemma":[0.00003284963,0.00008482323,0.00004637903,0.0003515689,0.00002401626,0.0001550979,0.00007928481,0.00008532772,0.000004375089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001450858,"about_ca_system_score_gemma":0.00003659421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000565653,"about_ca_topic_score_gemma":0.0000021726,"domain_scores_codex":[0.9993106,0.00001877765,0.0001804036,0.0002251866,0.0001120622,0.0001529811],"domain_scores_gemma":[0.9994289,0.00008613904,0.000137012,0.0002128783,0.00007048167,0.00006459536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001224662,0.00003616793,0.00550869,0.0002733919,0.00005193624,0.000004955998,0.003367323,0.1415547,0.002070106,0.01874362,0.009981832,0.8182848],"study_design_scores_gemma":[0.0002271227,0.0004668497,0.0004788261,0.00003694453,0.0000103214,0.00001704906,0.00007059787,0.957881,0.03785969,0.0006803512,0.002102253,0.0001689764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5028688,0.00005797172,0.4958656,0.0002454806,0.0002634885,0.000210289,9.305052e-7,0.0003503567,0.0001371503],"genre_scores_gemma":[0.7227954,0.00002848292,0.2769354,0.00007253719,0.0001124051,0.000009354353,7.140005e-7,0.000006961976,0.00003880573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8181158,"threshold_uncertainty_score":0.3458991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01245650318579186,"score_gpt":0.2333344045542637,"score_spread":0.2208779013684718,"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."}}