{"id":"W4248503218","doi":"10.1145/2508148.2485947","title":"Cooperative boosting","year":2013,"lang":"en","type":"article","venue":"ACM SIGARCH Computer Architecture News","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Boosting (machine learning); Workload; Computer science; Power management; Central processing unit; Performance improvement; Supercomputer; Parallel computing; Computer engineering; Coupling (piping); Thermal; Efficient energy use; Computational science; Power (physics); Operating system; Artificial intelligence; Engineering; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002317061,0.0003287346,0.0003124048,0.0002561802,0.0003209804,0.000598514,0.002860191,0.0001000088,0.0000495593],"category_scores_gemma":[0.0001894756,0.0002749605,0.0001173159,0.0006071147,0.00008613023,0.0004416296,0.001889576,0.0004908134,0.0002323111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004148313,"about_ca_system_score_gemma":0.00009054598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001421264,"about_ca_topic_score_gemma":0.000009114287,"domain_scores_codex":[0.9976022,0.0002707045,0.0003980024,0.0007427123,0.0003875277,0.0005988523],"domain_scores_gemma":[0.9973133,0.0005968145,0.0001263311,0.001464444,0.0002644557,0.0002346635],"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":[0.000003940344,0.00008961169,0.0006968984,0.00002003598,0.00005092389,0.00002220368,0.002902047,0.05401948,0.0009405144,0.006411599,0.04933609,0.8855066],"study_design_scores_gemma":[0.0008254005,0.000599147,0.003721918,0.0001208777,0.000007808624,0.0001788611,0.00001620549,0.9099184,0.004277746,0.04905577,0.03026896,0.001008947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005258209,0.0000795204,0.9831064,0.005932658,0.0003185832,0.0005345944,8.784e-7,0.001427907,0.00334126],"genre_scores_gemma":[0.1496706,0.00001034322,0.8455765,0.004035509,0.0003740532,0.00006263601,0.000006802344,0.0000234327,0.0002402019],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8844977,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141690242549186,"score_gpt":0.2497245611594903,"score_spread":0.2355555369045717,"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."}}