{"id":"W2998916130","doi":"10.1109/lwc.2020.2966624","title":"Nested Construction of Polar Codes for Blind Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Decoding methods; Control channel; Telecommunications link; Latency (audio); Polar; Scheme (mathematics); Coding (social sciences); Channel code; Real-time computing; Computer network; Algorithm; Telecommunications; Mathematics","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":[0.0006224776,0.0006763252,0.0005728881,0.0007695059,0.0006163236,0.0007058898,0.0005258459,0.0007253436,0.001593009],"category_scores_gemma":[0.003417997,0.0003159254,0.0003729533,0.0007392171,0.001059386,0.00117831,0.001957691,0.001101928,0.00066782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004691178,"about_ca_system_score_gemma":0.00118238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000867194,"about_ca_topic_score_gemma":0.0007940574,"domain_scores_codex":[0.9990171,0.0003063228,0.00005070715,0.0001444355,0.0003433933,0.0001380247],"domain_scores_gemma":[0.9980648,0.0008020722,0.0002305646,0.00034265,0.000449512,0.0001103681],"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":[0.0007785244,0.0001156967,0.001344496,0.0003029868,0.00006152339,0.0006137867,0.0006644476,0.188272,0.1318852,0.4068586,0.002654066,0.2664487],"study_design_scores_gemma":[0.00006545193,0.0003492404,0.0003084181,0.00008003752,0.0000327013,0.0007036136,0.00007705726,0.8410354,0.05605908,0.09022763,0.01096827,0.00009309251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01803195,0.0003168439,0.9788205,0.0001119989,0.00006724409,0.00004928271,0.00006390052,0.0001786622,0.002359544],"genre_scores_gemma":[0.4336278,0.0006559636,0.5615862,0.0002848947,0.00008593418,0.0001915198,0.0002190856,0.00005311861,0.003295514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001593009,"threshold_uncertainty_score":0.005329192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04607982118845658,"score_gpt":0.2875378777025291,"score_spread":0.2414580565140726,"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."}}