{"id":"W4393062321","doi":"10.1109/gcwkshps58843.2023.10465030","title":"Step-GRAND: A Low Latency Universal Soft-Input Decoder","year":2023,"lang":"en","type":"article","venue":"","topic":"Optical Network Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Latency (audio); Low latency (capital markets); Telecommunications; Computer network","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000473564,0.000105081,0.0001110186,0.0001011949,0.00002843771,0.00002058237,0.0001661127,0.0001162673,0.0001786655],"category_scores_gemma":[0.00002334353,0.0000939669,0.000039208,0.0004627091,0.00003730181,0.00008515477,0.00007255821,0.0001396499,0.001298362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002926888,"about_ca_system_score_gemma":0.000005624924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005750904,"about_ca_topic_score_gemma":0.00003231762,"domain_scores_codex":[0.9993738,0.000003883516,0.00009876637,0.0001209696,0.00008947394,0.0003131058],"domain_scores_gemma":[0.9996729,0.00006565055,0.000005056496,0.000198508,0.00001354591,0.00004429266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002936595,0.00006775129,0.004917449,0.0002597712,0.0003110477,0.0004323527,0.0002815583,0.1951208,0.001953716,0.08372185,0.4215257,0.2913787],"study_design_scores_gemma":[0.00042655,0.00003540918,0.001584935,0.00002663408,0.00001313149,0.000003341072,0.0001903455,0.9776911,0.0008174497,0.002940586,0.01594969,0.0003208904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8207936,0.0002128897,0.06494503,0.0009534391,0.0008797898,0.0002997537,0.000008931935,0.02823008,0.08367649],"genre_scores_gemma":[0.9875457,0.0001722442,0.009731047,0.00003294414,0.00004451851,0.000007236385,0.000005679189,0.00003814786,0.002422454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7825703,"threshold_uncertainty_score":0.9994792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007612434533068111,"score_gpt":0.1969952043100646,"score_spread":0.1893827697769964,"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."}}