{"id":"W2611384886","doi":"10.1109/lcomm.2017.2748940","title":"Blind Detection With Polar Codes","year":2017,"lang":"en","type":"preprint","venue":"IEEE Communications Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Decoding methods; Decodes; False alarm; Real-time computing; Constraint (computer-aided design); Latency (audio); Identifier; Error detection and correction; Algorithm; Wireless; Set (abstract data type); Computer network; Telecommunications; Artificial intelligence; 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":[0.001249754,0.001133173,0.0006970788,0.001122781,0.0006584768,0.00171371,0.0006330684,0.001250457,0.002229082],"category_scores_gemma":[0.008292849,0.0003471272,0.0003659834,0.001484312,0.001390992,0.001858187,0.001764025,0.001207737,0.001590939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006167117,"about_ca_system_score_gemma":0.0011445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001135535,"about_ca_topic_score_gemma":0.0007396583,"domain_scores_codex":[0.9980687,0.0006555817,0.00008603823,0.000318663,0.0006467376,0.0002242831],"domain_scores_gemma":[0.9965445,0.001642964,0.00038167,0.0005627773,0.0007950189,0.00007298874],"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":[0.001856787,0.0001101479,0.001783682,0.0005466745,0.00009124942,0.0004617516,0.0003926148,0.2111262,0.08722664,0.3602563,0.005291971,0.3308558],"study_design_scores_gemma":[0.00009586252,0.0003287802,0.0003692171,0.0001178564,0.00004760978,0.001035085,0.00007663613,0.7763378,0.07838728,0.126317,0.01676779,0.0001190551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007958101,0.0006087596,0.9870206,0.0001222572,0.00008420723,0.0000511225,0.00006253119,0.000240915,0.003851552],"genre_scores_gemma":[0.4143081,0.001500249,0.574595,0.0004678015,0.0001426053,0.0002202027,0.0002430307,0.0001062731,0.008416678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002229082,"threshold_uncertainty_score":0.007457078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0600659378126206,"score_gpt":0.3191707891389228,"score_spread":0.2591048513263022,"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."}}