{"id":"W2071868606","doi":"10.1145/966809.966810","title":"Aliasing and anti-aliasing in branch history table prediction","year":2003,"lang":"en","type":"article","venue":"ACM SIGARCH Computer Architecture News","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Aliasing; Computer science; Table (database); Algorithm; Branch predictor; Lookup table; Parallel computing; Artificial intelligence; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003057997,0.0009055121,0.0009247895,0.001667319,0.0007827593,0.001748775,0.001907456,0.001019961,0.001729109],"category_scores_gemma":[0.02844968,0.0009323893,0.0006736099,0.002486638,0.0008204444,0.00502783,0.001333523,0.001750975,0.0007577051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008887919,"about_ca_system_score_gemma":0.002135949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007903283,"about_ca_topic_score_gemma":0.01154429,"domain_scores_codex":[0.9958753,0.001136362,0.0002651683,0.0006074813,0.00176598,0.0003497913],"domain_scores_gemma":[0.9746116,0.01494118,0.003116531,0.004050033,0.002920231,0.0003604235],"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":[0.001018502,0.0003706786,0.07283429,0.0003337703,0.00016723,0.0003084593,0.0003419445,0.4665878,0.03002042,0.01216704,0.005999432,0.4098505],"study_design_scores_gemma":[0.00001982006,0.0001134358,0.003431838,0.00001499495,0.00002842997,0.000129686,0.00002642323,0.9771952,0.01323056,0.004125171,0.001652029,0.0000324491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1328713,0.001025581,0.8577805,0.0004824536,0.00008369898,0.00008942654,0.000326595,0.005094924,0.002245573],"genre_scores_gemma":[0.8156235,0.0004581662,0.1809008,0.000216638,0.00008142866,0.00007844641,0.0005221172,0.0004714878,0.001647457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007903283,"threshold_uncertainty_score":0.01617247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01675589333547661,"score_gpt":0.2346537192843288,"score_spread":0.2178978259488522,"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."}}