{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006906728,0.0007910848,0.0009534523,0.0005815903,0.0009911772,0.001695156,0.00173814,0.0008943595,0.003457928],"category_scores_gemma":[0.002262171,0.000570646,0.0005400708,0.001514981,0.0007192875,0.00210657,0.001248868,0.001050954,0.001514908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007684603,"about_ca_system_score_gemma":0.001081222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006228732,"about_ca_topic_score_gemma":0.007499549,"domain_scores_codex":[0.9989412,0.0001751066,0.00007043996,0.0003047669,0.000271449,0.0002369813],"domain_scores_gemma":[0.99867,0.0004192073,0.00008283702,0.0004550669,0.0002777834,0.00009511034],"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":[0.003828897,0.0006975802,0.008601552,0.0009327864,0.0002965629,0.001898704,0.0009962649,0.2449735,0.2127437,0.04571999,0.0616211,0.4176893],"study_design_scores_gemma":[0.0001549971,0.0003466034,0.001602676,0.00002474896,0.00005000414,0.0003746346,0.000181552,0.9134941,0.04559226,0.02085043,0.0172832,0.00004485785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2100183,0.00192537,0.7659125,0.0008077856,0.0002145082,0.0002415569,0.0007704635,0.01033336,0.009776279],"genre_scores_gemma":[0.7292715,0.0008883856,0.2608774,0.0002732959,0.0001337942,0.0002235236,0.001532868,0.0005583543,0.006240912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006228732,"threshold_uncertainty_score":0.01238495,"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."}}