{"id":"W2139656913","doi":"10.1109/tsp.2009.2022915","title":"Optimal Threshold Policies for Multivariate POMDPs in Radar Resource Management","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Multilinear map; Parameterized complexity; Markov decision process; Computer science; Mathematical optimization; Scheduling (production processes); Multivariate statistics; Markov process; Radar; Markov chain; Algorithm; Mathematics; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002075775,0.0001880385,0.0001744506,0.0002733734,0.0003518366,0.0002901507,0.0003610599,0.00008104794,0.000005541962],"category_scores_gemma":[6.277155e-7,0.0001848175,0.00009827148,0.0006953186,0.00002355441,0.0004194207,0.000002283578,0.0002266667,0.000006236676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009089856,"about_ca_system_score_gemma":0.00002817991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007250528,"about_ca_topic_score_gemma":0.000002586979,"domain_scores_codex":[0.9986018,0.00002722424,0.0002955036,0.0004179411,0.0002583953,0.0003991353],"domain_scores_gemma":[0.9995227,0.00004514574,0.00007607222,0.0002132229,0.00005268557,0.00009013489],"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":[0.0001062734,0.0003912075,5.324695e-7,0.00002765392,0.00002102705,0.00001886334,0.0005360114,0.3856851,0.0005660364,0.0008734885,0.000141199,0.6116326],"study_design_scores_gemma":[0.001346983,0.0003128026,0.0001176072,0.0001249648,0.00002082033,0.00001880806,0.0001814928,0.9837844,0.009101802,0.00104674,0.003602602,0.0003409741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001498703,0.00006065104,0.995837,0.000634264,0.0001140563,0.000309951,0.000007074061,0.0003159184,0.001222416],"genre_scores_gemma":[0.905126,0.000007626927,0.09399769,0.0005395473,0.00005585779,0.00003171272,0.000001486801,0.00001191761,0.0002280951],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9036273,"threshold_uncertainty_score":0.7536637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02004058503293873,"score_gpt":0.2655551955787573,"score_spread":0.2455146105458186,"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."}}