{"id":"W4232808664","doi":"10.1109/iwsoc.2004.1319875","title":"A step towards intelligent translation from high-level design to RTL","year":2004,"lang":"en","type":"article","venue":"","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Translation (biology); Computer architecture; Software engineering; Programming language","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":[0.0004576538,0.0001850063,0.000209897,0.0001658188,0.00005597664,0.0001730859,0.0009776975,0.000107347,0.00004066745],"category_scores_gemma":[0.00002408297,0.0001631506,0.00006416758,0.0004237415,0.00001425895,0.0004217643,0.00009544367,0.00009978696,0.000308214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000186564,"about_ca_system_score_gemma":0.0001388444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002435146,"about_ca_topic_score_gemma":0.0001345838,"domain_scores_codex":[0.9983398,0.0001213725,0.00036239,0.0004939627,0.0004113058,0.0002711308],"domain_scores_gemma":[0.9989057,0.00008728477,0.00005853784,0.0006979423,0.00009355531,0.000156939],"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":[0.00003409855,0.0001927327,0.00002098019,0.00001471094,0.00006075171,0.0000354262,0.007353634,0.002929103,0.03617694,0.1527187,0.006686392,0.7937765],"study_design_scores_gemma":[0.000453514,0.0004406833,0.0004483992,0.0001096173,0.000009408713,0.000008998282,0.00005757115,0.0115155,0.9062541,0.07737318,0.002795557,0.000533527],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006114474,0.00006312273,0.9943236,0.001471646,0.0003749988,0.0007217402,0.000005153895,0.0009487709,0.001479542],"genre_scores_gemma":[0.3921878,0.000003355781,0.6071236,0.0004319475,0.00007808378,0.00005065036,0.000001719116,0.00001014959,0.0001127968],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8700771,"threshold_uncertainty_score":0.665309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1147232984548209,"score_gpt":0.294202456713398,"score_spread":0.1794791582585771,"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."}}