{"id":"W2288813195","doi":"10.1109/glocomw.2015.7414184","title":"Low Complexity Techniques for SCMA Detection","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Codebook; Computer science; Decoding methods; Computational complexity theory; Code word; Message passing; Reduction (mathematics); Algorithm; Code (set theory); Multiplexing; Theoretical computer science; Computer engineering; Parallel computing; Telecommunications; 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":[],"consensus_categories":[],"category_scores_codex":[0.00006051608,0.00005959432,0.0000676466,0.00005250794,0.00002870448,0.000009511402,0.0001649874,0.00005692885,0.000004236706],"category_scores_gemma":[0.0000560644,0.00005855259,0.00001772307,0.00008832674,0.00004534619,0.000106883,0.00003776707,0.00006692261,0.00001119406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006929739,"about_ca_system_score_gemma":0.000003427989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004343899,"about_ca_topic_score_gemma":0.00004163888,"domain_scores_codex":[0.9997179,0.000003908051,0.0000873959,0.00005769205,0.00004155658,0.00009155275],"domain_scores_gemma":[0.9995931,0.00002493051,0.00001221542,0.0002910707,0.00005617477,0.00002247818],"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":[0.00001340225,0.00003121563,0.00004177809,0.00007526176,0.00001368405,2.137827e-7,0.00007263058,0.007430998,0.05313765,0.01978994,0.0058577,0.9135355],"study_design_scores_gemma":[0.00009331292,0.00003104799,0.00002574981,0.00000518385,9.334447e-7,0.000001260186,0.0001122292,0.03886066,0.9167191,0.02752528,0.01653344,0.00009176329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00463867,0.00006979061,0.9836545,0.00008428068,0.0000535495,0.0001906223,0.000002465539,0.005196079,0.006110023],"genre_scores_gemma":[0.8299325,0.00002414283,0.1698396,0.0000132883,0.00001176327,0.0001123738,0.000003324781,0.00001331485,0.00004969962],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9134437,"threshold_uncertainty_score":0.2387705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0674419449922478,"score_gpt":0.2876614432139348,"score_spread":0.220219498221687,"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."}}