{"id":"W2801133560","doi":"10.1109/lcomm.2018.2829706","title":"Capacity-Approaching Variable-Length Pearson Codes","year":2018,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Computer science; Redundancy (engineering); Algorithm; Pearson product-moment correlation coefficient; Variable (mathematics); Variable-length code; Theoretical computer science; Mathematics; Decoding methods; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003153832,0.0002006637,0.0001961866,0.0001732814,0.0004562181,0.00006789437,0.002020359,0.00008631469,0.00002374522],"category_scores_gemma":[0.00004526593,0.0002335444,0.00005199115,0.0004095369,0.0004759975,0.0003639544,0.0002097337,0.0005589087,0.00009960194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001587355,"about_ca_system_score_gemma":0.00001180176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000502827,"about_ca_topic_score_gemma":0.00003975324,"domain_scores_codex":[0.9988899,0.000178274,0.0003261812,0.0001705801,0.0001453996,0.0002896723],"domain_scores_gemma":[0.9958864,0.000247625,0.0000721304,0.003632912,0.00008980827,0.00007105265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001223981,0.0002300552,0.0007050372,0.000120231,0.0002709817,0.000001129271,0.00487175,0.01149142,0.8483999,0.05405426,0.03649369,0.04334933],"study_design_scores_gemma":[0.0008480008,0.00009077483,0.001135777,0.0003867072,0.00009762649,0.00005126795,0.000412935,0.3895414,0.157175,0.00514548,0.443201,0.001914028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.067436,0.0005317729,0.8936513,0.00339792,0.0003048909,0.0004278621,0.0000335061,0.003419962,0.03079677],"genre_scores_gemma":[0.6985534,0.0003777743,0.3001573,0.0006246074,0.00007323272,0.0001135044,0.00002726292,0.0000522945,0.00002063111],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6912249,"threshold_uncertainty_score":0.9523664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03745906696476733,"score_gpt":0.2626898693912623,"score_spread":0.225230802426495,"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."}}