{"id":"W4238035143","doi":"10.1145/566225.513844","title":"Automatic formal verification for scheduled VLIW code","year":2002,"lang":"en","type":"article","venue":"ACM SIGPLAN Notices","topic":"Embedded Systems Design Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Very long instruction word; Computer science; Compiler; Parallel computing; Code generation; Instruction-level parallelism; Software pipelining; Code (set theory); Programming language; Instruction scheduling; Optimizing compiler; Parallelism (grammar); Computer architecture; Operating system; Dynamic priority scheduling; Key (lock); Schedule","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":[0.002849763,0.0006184298,0.0003789718,0.0009155409,0.0007828858,0.001498023,0.001677564,0.0007185494,0.002594285],"category_scores_gemma":[0.01568551,0.0005772157,0.001127695,0.0003517637,0.002510868,0.002011803,0.001258077,0.001107546,0.0006400808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139031,"about_ca_system_score_gemma":0.003409153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003754554,"about_ca_topic_score_gemma":0.003516232,"domain_scores_codex":[0.9967243,0.0009436781,0.0002543654,0.0003533734,0.001384784,0.0003394278],"domain_scores_gemma":[0.9854568,0.009980404,0.001024257,0.001480202,0.001926625,0.0001317681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004329343,0.0002598043,0.004207713,0.001060914,0.0001047766,0.001133814,0.001461378,0.3481162,0.1007618,0.377304,0.004848403,0.1603082],"study_design_scores_gemma":[0.0001828049,0.0001875234,0.0003798568,0.000126063,0.00005469711,0.0002246212,0.0001033354,0.7856939,0.0840563,0.1176,0.01133608,0.00005493403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0294521,0.00007081965,0.9636986,0.0001990908,0.00004817162,0.0001355202,0.000171367,0.004444696,0.001779628],"genre_scores_gemma":[0.4863667,0.0002334926,0.5093307,0.0001701383,0.00006373334,0.0003880424,0.0006432701,0.0006165308,0.002187278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003754554,"threshold_uncertainty_score":0.01507115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06075935433410936,"score_gpt":0.2852430740329052,"score_spread":0.2244837196987958,"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."}}