{"id":"W4247144579","doi":"10.1145/3140659.3080216","title":"LogCA","year":2017,"lang":"en","type":"article","venue":"ACM SIGARCH Computer Architecture News","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"Scheme for Promotion of Academic and Research Collaboration; University of Wisconsin-Madison; National Science Foundation","keywords":"Computer science; Oracle; Interface (matter); Variety (cybernetics); Computer architecture; Embedded system; Efficient energy use; Parallel computing; Computer engineering; Software engineering; Artificial intelligence","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":[0.001488299,0.0008744643,0.000550653,0.001278746,0.001036727,0.004640956,0.002254324,0.001467553,0.1423613],"category_scores_gemma":[0.0054139,0.0006160949,0.0007719311,0.001364811,0.0007178457,0.004403103,0.001970984,0.002197106,0.07858764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002819286,"about_ca_system_score_gemma":0.00344052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005706951,"about_ca_topic_score_gemma":0.006950013,"domain_scores_codex":[0.9987477,0.0001876451,0.00006994119,0.0002059021,0.0006483408,0.0001405134],"domain_scores_gemma":[0.9970034,0.0004810189,0.0001338667,0.001087728,0.001092016,0.000202055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003116645,0.000125252,0.001543158,0.0003143398,0.00001975987,0.0001833766,0.0001664196,0.01346658,0.003969064,0.1810666,0.5338022,0.2650316],"study_design_scores_gemma":[0.00003205583,0.00004397119,0.0003299755,0.00006649863,0.000006948128,0.0001808579,0.00003786359,0.0294601,0.002101386,0.02354294,0.9441723,0.0000250965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.009572777,0.002391711,0.3232825,0.01030194,0.002942846,0.0005731718,0.01065695,0.08147168,0.5588065],"genre_scores_gemma":[0.1148932,0.004050023,0.1931504,0.004815452,0.0008155837,0.001025603,0.02331468,0.01413919,0.6437958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1423613,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02216156391471025,"score_gpt":0.2852843903068958,"score_spread":0.2631228263921855,"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."}}