{"id":"W2798422034","doi":"10.1145/3183713.3183734","title":"Pipelined Query Processing in Coprocessor Environments","year":2018,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Banting and Best Diabetes Centre, University of Toronto","keywords":"Coprocessor; Computer science; Throughput; Bandwidth (computing); Parallel computing; Computer architecture; Data processing; Database; Operating system; Computer network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001966309,0.00007724739,0.00008175067,0.00009809518,0.00007131902,0.00007943037,0.0004495724,0.00004040997,0.00001685811],"category_scores_gemma":[0.00002173791,0.00006895015,0.0000131408,0.0002987063,0.00004187021,0.0003105995,0.0001376404,0.00005655609,0.00006567761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002647321,"about_ca_system_score_gemma":0.00003579694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001347862,"about_ca_topic_score_gemma":0.000006527529,"domain_scores_codex":[0.9992462,0.0000234069,0.000170233,0.0002525764,0.0001332778,0.0001742999],"domain_scores_gemma":[0.9996688,0.000009754158,0.0000504223,0.0002096994,0.00002474712,0.00003660132],"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":[0.00005499678,0.001063956,0.02190423,0.0001037741,0.00001333733,0.00004802315,0.006772577,0.005786999,0.007907311,0.03360469,0.02275323,0.8999869],"study_design_scores_gemma":[0.0002695102,0.00006659611,0.002361118,0.00003533799,7.44432e-7,0.000004969709,0.00001116584,0.9733059,0.01581609,0.002690945,0.005246621,0.000191007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003442782,0.00004353154,0.9830685,0.0002966015,0.00004630227,0.00006560438,6.817783e-8,0.000320668,0.012716],"genre_scores_gemma":[0.6885713,0.000006468029,0.3096928,0.0005450461,0.00003800578,0.000005445465,5.175355e-7,0.000004049131,0.001136347],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9675189,"threshold_uncertainty_score":0.2811705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411943387027256,"score_gpt":0.2623548918001984,"score_spread":0.2482354579299259,"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."}}