{"id":"W2103584928","doi":"10.1109/synasc.2005.56","title":"Performance analysis of generics in scientific computing","year":2005,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Benchmark (surveying); Compiler; Computer science; Suite; Implementation; Java; Programming language; Template; Test suite; Generic programming; Software engineering; Theoretical computer science; Test case; Machine learning","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":[0.009456407,0.001199904,0.001275638,0.001962467,0.001347819,0.00311122,0.003204333,0.001396857,0.003131537],"category_scores_gemma":[0.04229735,0.0008202682,0.001384267,0.004992622,0.002014126,0.00584583,0.002477035,0.001592163,0.001298825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002787205,"about_ca_system_score_gemma":0.002339347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002565054,"about_ca_topic_score_gemma":0.001561737,"domain_scores_codex":[0.9900226,0.002654788,0.000820759,0.001270111,0.004149234,0.001082621],"domain_scores_gemma":[0.9520965,0.01951413,0.004906421,0.01664072,0.005501358,0.001340832],"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":[0.004490457,0.0008160329,0.06333189,0.002012928,0.0005395989,0.0004947609,0.0009985452,0.4779288,0.05190224,0.0879411,0.01880044,0.2907432],"study_design_scores_gemma":[0.0002906961,0.001948712,0.01539388,0.0001478707,0.0003149955,0.0005894709,0.0003196249,0.8120241,0.1128208,0.02788254,0.02807299,0.0001942979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8102338,0.003932885,0.1449598,0.0008375703,0.0002586677,0.0003683428,0.001039487,0.01799852,0.02037087],"genre_scores_gemma":[0.8932654,0.001033221,0.09903722,0.0001974907,0.00007419567,0.000182813,0.001988407,0.002266121,0.001955186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009456407,"threshold_uncertainty_score":0.05001086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01840072268777248,"score_gpt":0.2623674235391358,"score_spread":0.2439667008513633,"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."}}