{"id":"W2000185635","doi":"10.1006/jpdc.2002.1861","title":"Efficient Selection and Sorting Schemes Using Coteries for Processing Large Distributed Files","year":2002,"lang":"en","type":"article","venue":"Journal of Parallel and Distributed Computing","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Sorting; Node (physics); Quicksort; Sorting algorithm; Key (lock); Selection (genetic algorithm); Ranking (information retrieval); Scheme (mathematics); Theoretical computer science; Binary logarithm; Sorting network; Distributed computing; Computer network; sort; Algorithm; Mathematics; Discrete mathematics; Information retrieval; Operating system; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0006173514,0.0002260261,0.0004445755,0.00009939355,0.0006952356,0.0005098975,0.0002505328,0.00009449454,0.000002511068],"category_scores_gemma":[0.0001396227,0.0001962595,0.0001013533,0.0003971696,0.0000517653,0.0003224391,0.0001465637,0.0002077652,3.911363e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006038138,"about_ca_system_score_gemma":0.00004224638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004564746,"about_ca_topic_score_gemma":0.000001200325,"domain_scores_codex":[0.9980928,0.00006129598,0.0007957874,0.0003077286,0.000274408,0.0004680284],"domain_scores_gemma":[0.9982563,0.0001285316,0.0008959101,0.0001091976,0.0004382842,0.0001717559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004167553,0.001962532,0.05394758,0.002567343,0.0007449185,0.0002361785,0.007772049,0.6641773,0.01210862,0.04799606,0.005867127,0.2022035],"study_design_scores_gemma":[0.001280899,0.0001193924,0.002287564,0.0003681248,0.00003006222,0.0004560223,0.0002862251,0.992139,0.00008757447,0.0002309994,0.002482158,0.0002319358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3139744,0.00211855,0.6833919,0.0001681873,0.0001190376,0.0001138788,0.0000686022,0.00003884672,0.000006573066],"genre_scores_gemma":[0.9466567,0.00002106953,0.0530792,0.00003303553,0.0001777983,0.000001833207,0.00001644531,0.000009674945,0.00000421324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6326823,"threshold_uncertainty_score":0.8003229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461302314226602,"score_gpt":0.2677037008635714,"score_spread":0.2430906777213054,"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."}}