{"id":"W2158613955","doi":"10.1007/s00245-007-9007-8","title":"Robust Dynamics and Control of a Partially Observed Markov Chain","year":2007,"lang":"en","type":"article","venue":"Applied Mathematics & Optimization","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Markov chain; Mathematics; Stochastic differential equation; Markov process; Applied mathematics; Ordinary differential equation; Series (stratigraphy); Optimal control; Gaussian; Differential equation; Mathematical optimization; Mathematical analysis; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001935979,0.0010034,0.001813178,0.001056912,0.0008448098,0.002445141,0.001899639,0.001777194,0.002897098],"category_scores_gemma":[0.009040053,0.0009486683,0.0011009,0.0008402278,0.003401869,0.002095956,0.002547685,0.001760437,0.0003293473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002314976,"about_ca_system_score_gemma":0.002256854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01451398,"about_ca_topic_score_gemma":0.006362177,"domain_scores_codex":[0.9988768,0.000312924,0.0000434446,0.0003523771,0.000243391,0.0001710557],"domain_scores_gemma":[0.9944776,0.003432191,0.001006079,0.0002522184,0.0005735873,0.0002584104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001236084,0.00003013392,0.0005305017,0.00005322477,0.00005512639,0.00009889959,0.0000830541,0.8635725,0.001374485,0.1299049,0.0003770073,0.003796585],"study_design_scores_gemma":[0.00001510481,0.00001486295,0.0001093825,0.00000506208,0.000006424414,0.000007992995,0.000004440599,0.9828006,0.0001267142,0.01678519,0.000114573,0.000009638027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08716083,0.0004805834,0.905032,0.001280111,0.0001259449,0.00006162653,0.0003174173,0.0002224272,0.005319094],"genre_scores_gemma":[0.9719437,0.0004063522,0.02036833,0.0001149367,0.0001108505,0.0001329176,0.0002175281,0.00004703096,0.006658311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01451398,"threshold_uncertainty_score":0.02885896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834464625325062,"score_gpt":0.2117027576023668,"score_spread":0.1933581113491162,"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."}}