{"id":"W2157587788","doi":"10.1109/isit.2008.4595148","title":"Coding over an erasure channel with a large alphabet size","year":2008,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Erasure; Decoding methods; Binary erasure channel; Channel (broadcasting); Erasure code; Separable space; Alphabet; Coding (social sciences); Discrete mathematics; Mathematics; Algorithm; Computer science; Combinatorics; Statistics; Channel capacity; Telecommunications","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":[0.0002646039,0.0001532489,0.0001528539,0.00006560721,0.0002161968,0.000064501,0.0006865726,0.00006023303,0.00004190769],"category_scores_gemma":[0.00004638442,0.0001199445,0.0000345447,0.0003596872,0.000029651,0.0007507642,0.0001742642,0.0001649267,0.00001688451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003343459,"about_ca_system_score_gemma":0.00005882714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000825691,"about_ca_topic_score_gemma":0.0001522426,"domain_scores_codex":[0.9987496,0.00004769308,0.000128319,0.00040912,0.0003062718,0.0003590383],"domain_scores_gemma":[0.9990159,0.00009410749,0.00005883169,0.000636726,0.00008700726,0.0001074061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003033485,0.00458296,0.360123,0.0002226004,0.0003998069,0.00392344,0.08484185,0.0003373692,0.04448873,0.3006396,0.1730092,0.02712819],"study_design_scores_gemma":[0.005448375,0.004744775,0.2388404,0.0004190088,0.00004514251,0.00322567,0.0006187222,0.5690137,0.1442822,0.009078254,0.01968337,0.004600448],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2210107,0.00002011137,0.766808,0.0002836715,0.0001573474,0.0001666546,0.000001210438,0.002286489,0.009265859],"genre_scores_gemma":[0.900757,0.000005643064,0.09721522,0.0009626039,0.00005071564,0.00001410872,6.213633e-7,0.00001548604,0.0009786106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6797463,"threshold_uncertainty_score":0.4891195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183221334595005,"score_gpt":0.2551492166273716,"score_spread":0.2333170032814215,"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."}}