{"id":"W2101792768","doi":"10.5555/2337223.2337439","title":"Using the GPGPU for scaling up mining software repositories","year":2012,"lang":"en","type":"article","venue":"International Conference on Software Engineering","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; General-purpose computing on graphics processing units; Cloud computing; Eclipse; Software; Graphics; Field (mathematics); Graphics processing unit; Data science; Operating system","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":[0.001606369,0.001370983,0.0007161392,0.002270028,0.0006909019,0.00192257,0.002898257,0.0008786969,0.002647533],"category_scores_gemma":[0.01223593,0.0009013179,0.0008303797,0.004526687,0.0006825182,0.003755906,0.002649941,0.001618229,0.001749946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009185208,"about_ca_system_score_gemma":0.001470262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009973555,"about_ca_topic_score_gemma":0.008014836,"domain_scores_codex":[0.9979175,0.0005574015,0.000150116,0.0004878811,0.000640656,0.0002464977],"domain_scores_gemma":[0.9962165,0.00118993,0.0002322235,0.001367523,0.0007237595,0.0002702244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001286261,0.0007179584,0.04587466,0.0006669984,0.0006291512,0.0006459331,0.001429088,0.07462995,0.05069959,0.01108687,0.0518422,0.7604913],"study_design_scores_gemma":[0.0005672866,0.000890189,0.01999971,0.0001612427,0.0002980666,0.0006637726,0.001111549,0.8574965,0.04560011,0.02239378,0.0506275,0.0001902747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4969463,0.00286716,0.4329498,0.002744754,0.0008918197,0.0009788933,0.001577309,0.04348689,0.01755719],"genre_scores_gemma":[0.4153733,0.0008276805,0.5780113,0.0004494111,0.0000829031,0.0005406538,0.001886145,0.001182056,0.001646553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009973555,"threshold_uncertainty_score":0.019831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08929231936567784,"score_gpt":0.336572874856321,"score_spread":0.2472805554906432,"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."}}