{"id":"W2782317874","doi":"10.1109/tcsii.2018.2872653","title":"Design and Implementation of a Polar Codes Blind Detection Scheme","year":2018,"lang":"en","type":"preprint","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Decoding methods; Polar code; Wireless; Polar; Latency (audio); False alarm; Code (set theory); Real-time computing; Scheme (mathematics); Set (abstract data type); Algorithm; Computer engineering; Computer hardware; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001196596,0.0005436403,0.0007141565,0.0007245492,0.00050653,0.0003302792,0.001052695,0.0005306281,0.00001019692],"category_scores_gemma":[0.0000138087,0.0006134269,0.0001860755,0.0004370529,0.0001445774,0.0005483077,0.0000532117,0.0007761912,0.000006796241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002521189,"about_ca_system_score_gemma":0.0003215249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002553742,"about_ca_topic_score_gemma":0.0001447202,"domain_scores_codex":[0.9959989,0.0006089711,0.0009990878,0.001216842,0.0007250063,0.0004512186],"domain_scores_gemma":[0.9967402,0.0002806659,0.0008342004,0.001460104,0.0005276951,0.0001571713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001891637,0.0009628435,0.0001553181,0.003478161,0.001228593,0.00003182673,0.03014426,0.02544527,0.7447034,0.0006889577,0.0009065756,0.1920656],"study_design_scores_gemma":[0.001180206,0.001049188,0.0001220501,0.001457276,0.0001415655,0.0001685789,0.0004022694,0.0637599,0.9296179,0.0005459546,0.0004667273,0.001088387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05726708,0.0003034868,0.9365989,0.00003487113,0.002675757,0.002091749,0.00008114628,0.0008904233,0.00005661196],"genre_scores_gemma":[0.9790599,0.00008712756,0.01986111,0.0000274438,0.0001328742,0.0006806105,0.000004784823,0.00006657933,0.00007962042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9217927,"threshold_uncertainty_score":0.9996317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03898881336223481,"score_gpt":0.298916537268396,"score_spread":0.2599277239061612,"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."}}