{"id":"W2024128585","doi":"10.1109/iccnc.2013.6504198","title":"Polar codes for data storage applications","year":2013,"lang":"en","type":"article","venue":"2013 International Conference on Computing, Networking and Communications (ICNC)","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Decoding methods; Encoding (memory); Polar; Computer data storage; Error detection and correction; Throughput; Algorithm; Theoretical computer science; Computer engineering; Telecommunications; Computer hardware; Artificial intelligence","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":[0.0004746226,0.0005893189,0.0004280073,0.0007547958,0.0007317265,0.001687311,0.000527003,0.001045443,0.005784951],"category_scores_gemma":[0.003111905,0.0002105485,0.000238683,0.001419951,0.00102259,0.001794992,0.001144426,0.001670654,0.002259456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006663451,"about_ca_system_score_gemma":0.0009152775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005028052,"about_ca_topic_score_gemma":0.0005181615,"domain_scores_codex":[0.9994773,0.0001159553,0.00002966623,0.00006261133,0.0002625585,0.00005188677],"domain_scores_gemma":[0.9986873,0.0005133809,0.0001092442,0.0002575259,0.0003876942,0.00004481603],"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":[0.0001403507,0.00003531879,0.0003690569,0.000654455,0.000014661,0.0002375432,0.0002576697,0.02005267,0.02463402,0.7773022,0.01614841,0.1601536],"study_design_scores_gemma":[0.00003403487,0.0001452449,0.0002954871,0.0005142029,0.00002766962,0.001263305,0.0002001274,0.1236366,0.05141548,0.5566619,0.2657367,0.00006922788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01724139,0.01750572,0.8887663,0.002977028,0.001073274,0.0001821002,0.0004714086,0.0009887237,0.07079405],"genre_scores_gemma":[0.4692405,0.03028051,0.4608763,0.002133222,0.001246027,0.0005172749,0.000752675,0.0004197358,0.03453364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005784951,"threshold_uncertainty_score":0.01935261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1218389801662435,"score_gpt":0.3524047824314154,"score_spread":0.2305658022651719,"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."}}