{"id":"W2783785065","doi":"10.1109/lwc.2018.2792023","title":"Optimal Epidemic Information Dissemination in Uncertain Dynamic Environment","year":2018,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Markov chain; Computer science; Reliability (semiconductor); Dynamic programming; Markov decision process; Epidemic model; Markov process; Dissemination; Mathematical optimization; Stochastic programming; Continuous-time Markov chain; Stochastic process; Markov model; Stochastic modelling; Variable-order Markov model; Mathematics; Population; Machine learning; Algorithm","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":[0.0004923949,0.0001479835,0.0001592339,0.0002093452,0.0002419138,0.0001103048,0.001766098,0.00007831198,0.00001101884],"category_scores_gemma":[0.000006517114,0.0001578972,0.0000446594,0.0003447608,0.0002804347,0.001053712,0.0003062758,0.0002561136,0.0001832064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002197782,"about_ca_system_score_gemma":0.00003791454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005590259,"about_ca_topic_score_gemma":0.0000231859,"domain_scores_codex":[0.9986374,0.0001795063,0.0004778022,0.0002002066,0.0002229834,0.0002820546],"domain_scores_gemma":[0.9976441,0.0002738733,0.0001978996,0.001768915,0.00004182499,0.00007342735],"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":[0.00002969834,0.0003065432,0.001874649,0.00002948466,0.00004451725,0.00001118613,0.008523375,0.0108293,0.007409588,0.014345,0.01093878,0.9456579],"study_design_scores_gemma":[0.0001903738,0.00002438242,0.001281627,0.00005401019,0.000004545971,0.000009137477,0.00007951097,0.9945621,0.00008472303,0.0001274701,0.003396156,0.0001859756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1095233,0.00004527806,0.878583,0.01071347,0.0001912581,0.0002079737,0.000005501844,0.00008881176,0.0006414215],"genre_scores_gemma":[0.9448125,0.0002013568,0.05325519,0.001519109,0.00002757772,0.00006992469,0.00007545231,0.000007614405,0.00003127147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9837328,"threshold_uncertainty_score":0.6438861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712437688470638,"score_gpt":0.2710108525983225,"score_spread":0.2538864757136162,"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."}}