{"id":"W2149172516","doi":"10.1109/newcas.2008.4606324","title":"Scheduling of turbo decoding on a multiprocessor platform to manage its processing effort variability","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Computer science; Decoding methods; Scheduling (production processes); Multiprocessing; Fair-share scheduling; Dynamic priority scheduling; MPSoC; Rate-monotonic scheduling; Parallel computing; Two-level scheduling; Turbo; Embedded system; Real-time computing; Distributed computing; Quality of service; Algorithm; Computer network; Engineering","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":[0.0001898132,0.0001817392,0.0002340843,0.0001271282,0.0000949308,0.00001169938,0.0001368124,0.00008438476,0.00002244211],"category_scores_gemma":[0.0001061553,0.0001793102,0.00003733021,0.0004792397,0.00001736628,0.0003482231,0.00003487823,0.0001397227,0.00001531938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092515,"about_ca_system_score_gemma":0.00001343198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001509294,"about_ca_topic_score_gemma":0.000002731957,"domain_scores_codex":[0.9989436,0.000005703517,0.0003320716,0.0002437542,0.0001900953,0.0002848163],"domain_scores_gemma":[0.9994951,0.00008514635,0.00004839155,0.0001890038,0.00009170087,0.00009063652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002952659,0.00002461557,0.0007957129,0.0003186529,0.000009793881,0.000002934885,0.000388448,0.9890564,0.001567986,0.0001898156,0.000005400909,0.007610713],"study_design_scores_gemma":[0.000273593,0.00002492747,0.0007306523,0.0001911643,0.000006098185,0.000004331475,0.00003650794,0.9537378,0.04467698,0.00006908944,0.00003842392,0.0002104189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5208968,0.00003345212,0.4726788,0.000005075016,0.00006706257,0.0002455933,0.000001273832,0.0003294734,0.005742406],"genre_scores_gemma":[0.8467241,0.00002930871,0.1530409,0.00002963558,0.00004557543,0.0000264762,0.000006022707,0.00004106334,0.00005694104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3258273,"threshold_uncertainty_score":0.7312058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966968448213122,"score_gpt":0.235812982898971,"score_spread":0.2161432984168397,"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."}}