{"id":"W1587943761","doi":"10.48550/arxiv.1304.1790","title":"Upgraded Approximation of Non-Binary Alphabets for Polar Code Construction","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Alphabet; Channel (broadcasting); Polar; Binary number; Prime (order theory); Code (set theory); Algorithm; Computer science; Polar code; Mathematics; Arithmetic; Combinatorics; Telecommunications; Set (abstract data type); Decoding methods; Physics; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003037015,0.0002870419,0.0003935417,0.0004088746,0.0001196195,0.0000620961,0.001471583,0.0003707134,0.000006598015],"category_scores_gemma":[0.00006399386,0.0003523445,0.0002361831,0.0004323971,0.0001400634,0.0005239258,0.001012994,0.0003864993,0.00001113921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002012668,"about_ca_system_score_gemma":0.0001765408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002916787,"about_ca_topic_score_gemma":0.00001974474,"domain_scores_codex":[0.9983322,0.00009791645,0.000301551,0.0008848622,0.0001009038,0.000282535],"domain_scores_gemma":[0.9975794,0.0001486439,0.0006432701,0.001137685,0.0004011876,0.00008980406],"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":[0.0002477712,0.0009175436,0.0191767,0.003134171,0.0006814329,0.0000924856,0.003358633,0.07840142,0.02574754,0.8339226,0.008876494,0.02544316],"study_design_scores_gemma":[0.0003989454,0.0001464525,0.0009368931,0.0001838673,0.00007190632,0.000007343048,0.00009746946,0.8883148,0.01215972,0.09717365,0.0000929216,0.0004160173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.336886,0.00001664805,0.6610078,0.00005158807,0.0005290399,0.0006989649,0.00002279456,0.0003760884,0.0004110118],"genre_scores_gemma":[0.8741806,0.00003020733,0.1254554,0.00002219831,0.00004115737,0.000007582243,0.00003360203,0.00002014471,0.0002090337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8099134,"threshold_uncertainty_score":0.9998928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05864664668401657,"score_gpt":0.2079017112974834,"score_spread":0.1492550646134669,"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."}}