{"id":"W3088726914","doi":"10.1007/s10619-020-07313-y","title":"Introduction to the special issue on Self-managing and Hardware-Optimized Database Systems 2019","year":2020,"lang":"en","type":"article","venue":"Distributed and Parallel Databases","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; University of California, Davis; Institute of Computing Technology, Chinese Academy of Sciences; University of Pittsburgh; Chinese Academy of Sciences; Oracle","keywords":"Computer science; Database; Data structure; Computer architecture; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002972546,0.0001913949,0.0002082765,0.00004905571,0.0003727312,0.0003356459,0.0004557114,0.00001927052,0.00001271727],"category_scores_gemma":[0.0000988241,0.0001363773,0.00002919721,0.0003223089,0.00003110331,0.00007782555,0.0008115674,0.0001515117,0.00008102456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001651415,"about_ca_system_score_gemma":0.00001216091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001332745,"about_ca_topic_score_gemma":0.000004558121,"domain_scores_codex":[0.9984949,0.0001272967,0.0002107735,0.0006497895,0.0002503439,0.0002668718],"domain_scores_gemma":[0.9989811,0.0001220839,0.0000737382,0.0005915847,0.00003379326,0.0001977498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008317706,0.0000585783,0.00007739315,0.00007925097,0.00006154649,0.00003334364,0.000625061,0.05723728,0.000009731558,0.01035974,0.921768,0.009606977],"study_design_scores_gemma":[0.0003965056,0.00007638138,0.0002367105,0.00003571679,0.00002155064,0.00001057534,0.0002137966,0.2129392,0.00001191431,0.00000568068,0.785882,0.0001699752],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01343221,0.001434519,0.8370569,0.1416609,0.001986522,0.001223158,0.001788075,0.0006513546,0.0007663863],"genre_scores_gemma":[0.5014858,0.005141387,0.1738794,0.04712746,0.2457956,0.0005170348,0.02088494,0.0002895547,0.004878794],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6631775,"threshold_uncertainty_score":0.5561305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679472336439665,"score_gpt":0.2350329024298379,"score_spread":0.2182381790654413,"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."}}