{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0005828598,0.0003359811,0.0003250176,0.0003454177,0.0009231147,0.0008200197,0.01121755,0.0002306523,0.000001226645],"category_scores_gemma":[0.0000869397,0.0003358541,0.0001277769,0.0001869051,0.0003613126,0.0004462325,0.003347932,0.001608751,0.0000357853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002243867,"about_ca_system_score_gemma":0.0001526563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009876951,"about_ca_topic_score_gemma":0.001319213,"domain_scores_codex":[0.998091,0.0003327852,0.0003261919,0.0006167822,0.0003317846,0.0003014656],"domain_scores_gemma":[0.9853991,0.0002351968,0.0006496059,0.01341814,0.0002165253,0.00008146336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002530368,0.001816138,0.01233027,0.0009072223,0.002079292,0.0001818778,0.02136316,0.007153348,0.4117285,0.01070205,0.04856336,0.4829217],"study_design_scores_gemma":[0.004618827,0.001011676,0.01973879,0.007291534,0.0009734478,0.001028645,0.000275306,0.4150885,0.366746,0.03168364,0.1396433,0.01190029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0338681,0.0003572323,0.944775,0.0159997,0.0008691335,0.0006340227,0.00001278448,0.001651576,0.001832394],"genre_scores_gemma":[0.7608598,0.0001579627,0.2377844,0.0007890946,0.00007148244,0.0002347196,0.0000179993,0.00003412153,0.00005043261],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7269917,"threshold_uncertainty_score":0.9999093,"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."}}