{"id":"W2540934986","doi":"10.1016/j.procs.2016.09.356","title":"Epidemiology-based Task Assignment Algorithm for Distributed Systems","year":2016,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Context (archaeology); Population; Task analysis; Assignment problem; Distributed computing; Machine learning; Artificial intelligence; Medicine; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002603871,0.0002731149,0.0004088528,0.0001664732,0.0004141086,0.0001722854,0.002264876,0.0001029794,0.000002485413],"category_scores_gemma":[0.00007609455,0.0001778246,0.0001063835,0.0007388593,0.0005356359,0.000718514,0.0004409527,0.00009800459,0.00003847621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001809957,"about_ca_system_score_gemma":0.0007369357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000461702,"about_ca_topic_score_gemma":2.090596e-7,"domain_scores_codex":[0.9966112,0.00008400574,0.000567343,0.001185821,0.0005177087,0.001033878],"domain_scores_gemma":[0.9967257,0.001247307,0.0002942172,0.0008211724,0.0004298921,0.0004816803],"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":[0.000006564981,0.00009725006,0.0006875456,0.00002975051,0.00001200732,0.00001643918,0.00005413369,0.00100723,0.0001484368,0.02704306,0.005531865,0.9653657],"study_design_scores_gemma":[0.000633244,0.0002297821,0.0002517081,0.00009435836,0.000006336604,0.00003842633,0.000001871867,0.9916466,0.0001292649,0.00296361,0.003697636,0.0003072027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00008643523,0.0001681979,0.9938063,0.001995214,0.002882998,0.0006119328,0.00005952962,0.0003371146,0.00005226576],"genre_scores_gemma":[0.5108616,0.000007645252,0.4875226,0.0008813943,0.0004750994,0.0001756804,0.000008446878,0.0000105649,0.00005695199],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9906393,"threshold_uncertainty_score":0.7251476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03579302822350357,"score_gpt":0.2708897329434222,"score_spread":0.2350967047199186,"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."}}