{"id":"W3043302671","doi":"10.1109/sips50750.2020.9195254","title":"High-Throughput VLSI Architecture for GRAND","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Code word; Computer science; BCH code; Decoding methods; Code (set theory); Algorithm; Throughput; Code rate; Additive white Gaussian noise; Parallel computing; Channel (broadcasting); Telecommunications; Wireless","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":[0.0002246459,0.0007505958,0.0003891957,0.0008216642,0.0004203369,0.0009862173,0.001947881,0.0005195705,0.01754055],"category_scores_gemma":[0.0005468862,0.0002881479,0.0003216113,0.0006100229,0.0001874638,0.001052478,0.0006658064,0.0005006512,0.006120356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035063,"about_ca_system_score_gemma":0.001121526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002230491,"about_ca_topic_score_gemma":0.00373371,"domain_scores_codex":[0.9996911,0.00004328259,0.00001729384,0.00008081264,0.0001021435,0.00006539073],"domain_scores_gemma":[0.9997562,0.00004234351,0.00002406899,0.00005225491,0.000104194,0.00002096266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008739561,0.0004455979,0.004105902,0.0008538209,0.0002156871,0.0007460525,0.0003672421,0.03078571,0.2140381,0.06439519,0.0972603,0.5859125],"study_design_scores_gemma":[0.0003560938,0.002314078,0.003445978,0.0001890192,0.0003532108,0.001704212,0.0001926756,0.4794114,0.2092258,0.01850433,0.2840999,0.0002032994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1256087,0.004195969,0.731253,0.001598792,0.0009616812,0.0005806205,0.001110797,0.03013248,0.1045579],"genre_scores_gemma":[0.6578128,0.0008028203,0.2967069,0.0009523118,0.0002563862,0.0003990698,0.002081931,0.0003711532,0.04061657],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01754055,"threshold_uncertainty_score":0.05867898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06691802128240666,"score_gpt":0.3039410773495894,"score_spread":0.2370230560671828,"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."}}