{"id":"W4403600537","doi":"10.1109/taes.2024.3475991","title":"Task Scheduling in Cognitive Multifunction Radar Using Model-Based DRL","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; University of Toronto","funders":"Defence Research and Development Canada","keywords":"Computer science; Radar; Scheduling (production processes); Radar systems; Task (project management); Radar tracker; Processor scheduling; Engineering; Systems engineering; Telecommunications; Computer network","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":[0.0009447637,0.0001807371,0.0002379152,0.0004917057,0.0002549454,0.0003571732,0.0001218954,0.0001392985,0.00002615539],"category_scores_gemma":[0.00002191216,0.0001562607,0.0001020706,0.001111432,0.00005787141,0.0002309308,0.000001199713,0.0003774579,0.0000390369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002506722,"about_ca_system_score_gemma":0.0002481841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002305537,"about_ca_topic_score_gemma":0.0001552566,"domain_scores_codex":[0.9980534,0.0001006242,0.0004476106,0.0005793818,0.0004778315,0.000341094],"domain_scores_gemma":[0.9988776,0.000593514,0.00007415595,0.0002525942,0.0001237884,0.00007838845],"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":[0.00005251199,0.00007672318,0.00004373729,0.00001906018,0.00002172985,0.000001762974,0.0002826344,0.9713839,0.01111603,0.0009262376,0.00004397572,0.01603166],"study_design_scores_gemma":[0.0003741558,0.00007606353,0.00001238499,0.0001863978,0.00003035267,0.000009108294,0.0005693201,0.9922503,0.005013207,0.0007563462,0.0005522944,0.0001701245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2554833,0.0006647042,0.7428031,0.0001952087,0.0002130913,0.0004127561,0.00001996972,0.0001230083,0.0000848809],"genre_scores_gemma":[0.9982517,0.00006357348,0.000663454,0.0000631561,0.00004196762,0.0001255449,0.000002379446,0.00002453051,0.0007636663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7427685,"threshold_uncertainty_score":0.6372128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08376724659772532,"score_gpt":0.3826835461094865,"score_spread":0.2989162995117612,"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."}}