{"id":"W3025756901","doi":"10.1109/tcomm.2020.3039856","title":"Noisy Density Evolution With Asymmetric Deviation Models","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Communications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Low-density parity-check code; Code word; Large deviations theory; Decoding methods; Algorithm; Belief propagation; Range (aeronautics); Computer science; Code (set theory); Mathematics; Statistics","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":[0.0008119847,0.0005736355,0.0006817244,0.0004164677,0.0003582799,0.0008095826,0.000933819,0.0009685294,0.0005514767],"category_scores_gemma":[0.004745546,0.0002975313,0.0004653372,0.0004129325,0.001279899,0.001521532,0.0008554315,0.0009771223,0.0001622287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009861623,"about_ca_system_score_gemma":0.0005546493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001631165,"about_ca_topic_score_gemma":0.001013014,"domain_scores_codex":[0.9991871,0.0003212843,0.00003012712,0.00009549868,0.0002942448,0.00007181234],"domain_scores_gemma":[0.9981496,0.001096724,0.0002563654,0.0002530809,0.0001837577,0.00006047734],"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":[0.00007721048,0.00002832103,0.001336039,0.00004613976,0.00003321916,0.0002028617,0.00007560656,0.8897967,0.006779707,0.09634522,0.00022748,0.005051524],"study_design_scores_gemma":[0.000002394851,0.000008101428,0.00008563895,0.000002108894,0.000003135771,0.00002569078,0.000003541413,0.9917663,0.001214202,0.006794275,0.00008918785,0.00000546606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2587785,0.0004290066,0.7326934,0.0003439205,0.0000552642,0.00003348639,0.00009173868,0.0002307199,0.007343964],"genre_scores_gemma":[0.9809948,0.0001893035,0.01691211,0.00005838578,0.0000231888,0.00002426347,0.00005846925,0.00003098123,0.001708615],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001631165,"threshold_uncertainty_score":0.00715512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06863529664891711,"score_gpt":0.2877480401966405,"score_spread":0.2191127435477234,"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."}}