{"id":"W2125291845","doi":"10.1109/ipdps.2009.5160944","title":"A lightweight stream-processing library using MPI","year":2009,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Stream processing; Parallel computing; Computer architecture; 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":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009326914,0.0001439986,0.0001596109,0.00008476857,0.0001675626,0.000614529,0.0008177597,0.00005900849,0.00002527183],"category_scores_gemma":[0.0000044798,0.0001173466,0.00006107029,0.0005421941,0.00001216992,0.00108039,0.0001164399,0.0000908564,0.00005402793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001466255,"about_ca_system_score_gemma":0.0001016467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007583865,"about_ca_topic_score_gemma":1.976614e-7,"domain_scores_codex":[0.9988741,0.00004182987,0.000231002,0.0003412278,0.0001996956,0.0003120895],"domain_scores_gemma":[0.9993726,0.00002279775,0.00008171498,0.0003810215,0.00002780866,0.0001140587],"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":[0.00001724835,0.000520131,0.00389683,0.00009276686,0.0000460414,0.000319286,0.002162411,0.005028175,0.004825511,0.3691246,0.04873257,0.5652344],"study_design_scores_gemma":[0.0003233704,0.0001062709,0.001709098,0.0001479792,0.000005979674,0.0001158754,0.00002299139,0.9469445,0.002451069,0.00940082,0.03831638,0.0004556607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02021774,0.000404687,0.9103668,0.001104004,0.0002468322,0.00007701215,0.000001342973,0.001042326,0.06653928],"genre_scores_gemma":[0.9171188,0.000002112207,0.08090633,0.0007393414,0.0001835186,4.469178e-7,0.000004356971,0.000005940786,0.001039184],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9419163,"threshold_uncertainty_score":0.5925917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01921053736934865,"score_gpt":0.2449232835230519,"score_spread":0.2257127461537032,"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."}}