{"id":"W2884667593","doi":"10.4018/978-1-5225-3142-5.ch011","title":"Big Data in Massive Parallel Processing","year":2018,"lang":"en","type":"book-chapter","venue":"Advances in data mining and database management book series","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Big data; Computer science; Massively parallel; Cloud computing; Data processing; Domain (mathematical analysis); Field (mathematics); Distributed computing; Data science; Parallel computing; Database; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001067153,0.000585433,0.0005686555,0.0006025751,0.0002202883,0.0004338328,0.005748957,0.0001108943,0.00002556649],"category_scores_gemma":[0.00005233088,0.0005720523,0.00002453574,0.000203472,0.0003549965,0.00210822,0.02625266,0.0003199749,0.00002659052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005817131,"about_ca_system_score_gemma":0.00005138334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001998914,"about_ca_topic_score_gemma":0.0006722162,"domain_scores_codex":[0.9956196,0.00005316659,0.0007299562,0.002466134,0.000555157,0.0005760043],"domain_scores_gemma":[0.9933352,0.000114268,0.0004186551,0.006000139,0.00003193882,0.00009975342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007006055,0.00006923224,0.00009749478,0.00147798,0.00009169133,0.001251471,0.0006157116,0.0002400919,2.572108e-7,0.04357566,0.0240846,0.9284257],"study_design_scores_gemma":[0.0005644149,0.00005510247,0.00002523993,0.00281221,0.00005069397,0.00001501758,0.0003907855,0.0303687,5.20979e-7,0.002364416,0.9626608,0.0006921295],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.0001054281,0.1053278,0.1229017,0.002423896,0.002202494,0.00193745,0.001945549,0.0006650519,0.7624906],"genre_scores_gemma":[0.0004206376,0.06967186,0.474694,0.001717137,0.001240642,0.00008051828,0.01036266,0.0001908299,0.4416217],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9385762,"threshold_uncertainty_score":0.9996731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.050763890243285,"score_gpt":0.2797774737697361,"score_spread":0.2290135835264511,"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."}}