{"id":"W2081744702","doi":"10.1016/s1383-7621(01)00026-1","title":"A robust stack folding approach for Java processors: an operand extraction-based algorithm","year":2001,"lang":"en","type":"article","venue":"Journal of Systems Architecture","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Operand; Folding (DSP implementation); Algorithm; Java; Stack (abstract data type); Parallel computing; Bytecode; Call stack; Code (set theory); Benchmarking; Java bytecode; Programming language; Operating system; Java applet","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":[0.0006178666,0.001004337,0.001011844,0.0010442,0.0008042319,0.001056917,0.001660658,0.0009781851,0.005802758],"category_scores_gemma":[0.001482791,0.0005956577,0.0009214472,0.00094794,0.0005374068,0.001486413,0.001225736,0.001129133,0.001589467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005281389,"about_ca_system_score_gemma":0.001332539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002674753,"about_ca_topic_score_gemma":0.004280684,"domain_scores_codex":[0.9996405,0.00005497212,0.00002589186,0.00008935833,0.0001315528,0.00005771713],"domain_scores_gemma":[0.9995092,0.0001320214,0.00003970036,0.0001415131,0.0001523781,0.00002514327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005216705,0.0001662077,0.000995434,0.000142279,0.00008835383,0.0001478514,0.0001158686,0.1037177,0.0543461,0.01368306,0.005470991,0.8206045],"study_design_scores_gemma":[0.0000664805,0.0001017782,0.000267512,0.00001232862,0.00005393833,0.00006622858,0.00004079594,0.9596109,0.02440214,0.01230377,0.003049503,0.00002462887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01379258,0.00008502635,0.9806339,0.00009359002,0.00003168579,0.00005193484,0.0000452419,0.004018497,0.001247548],"genre_scores_gemma":[0.1099283,0.00006289715,0.8865982,0.00008094738,0.00001882114,0.00007775671,0.0001756144,0.0005012239,0.002556263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005802758,"threshold_uncertainty_score":0.01941216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03553081575273185,"score_gpt":0.281823797074327,"score_spread":0.2462929813215952,"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."}}