{"id":"W2546884927","doi":"10.1109/newcas.2016.7604820","title":"Performance characterization of an SCMA decoder","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Huawei Technologies","keywords":"Computer science; Decoding methods; Message passing; Latency (audio); Code (set theory); Wireless; Parallel computing; Implementation; Low latency (capital markets); Power consumption; Computer architecture; Computer engineering; Power (physics); Algorithm; Computer network; Telecommunications","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":[0.0004210755,0.0004951939,0.0003878189,0.0007237669,0.0003470031,0.0007771685,0.0004787349,0.0005865591,0.003032234],"category_scores_gemma":[0.003446507,0.0001593401,0.0001939236,0.0005156664,0.0002486873,0.0005770461,0.0002603907,0.00032925,0.000754639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009876799,"about_ca_system_score_gemma":0.001136661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608855,"about_ca_topic_score_gemma":0.002504578,"domain_scores_codex":[0.999225,0.0001076242,0.00006101385,0.0001126671,0.0003930909,0.0001005871],"domain_scores_gemma":[0.9972426,0.001328641,0.0002744058,0.0002352206,0.0008520447,0.0000671462],"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":[0.001511221,0.0002399333,0.00748413,0.0004416843,0.0001321518,0.0005545452,0.0002221019,0.2845435,0.6061447,0.0131012,0.001822465,0.08380244],"study_design_scores_gemma":[0.00003948488,0.0009466147,0.002234597,0.0000190408,0.00003777106,0.0004246673,0.00003938589,0.6972771,0.2956568,0.0008858096,0.002412902,0.00002584685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7637074,0.0007691929,0.2110146,0.0005254257,0.00006108999,0.0001997661,0.0009304063,0.001888346,0.02090383],"genre_scores_gemma":[0.976456,0.0001567874,0.02020147,0.00005066274,0.00001241125,0.00005247518,0.000313924,0.0000504366,0.002705743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003032234,"threshold_uncertainty_score":0.01014382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009232168153361897,"score_gpt":0.2081459989290419,"score_spread":0.19891383077568,"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."}}