{"id":"W4293863108","doi":"10.1109/siu55565.2022.9864811","title":"Time Resource Management in Cognitive Radars Based on Parameter Optimization","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Radar; Kalman filter; Radar tracker; Real-time computing; Waveform; Track (disk drive); Resource management (computing); Resource (disambiguation); Simulation; Artificial intelligence; Telecommunications","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":[0.0004127494,0.0002074348,0.00021381,0.0003269323,0.0009407992,0.0001719619,0.0005849606,0.0000527544,0.0002010805],"category_scores_gemma":[0.000005460201,0.0002376433,0.00003578191,0.000862331,0.0001527534,0.0001513642,0.0001915521,0.0004728088,0.00001053201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001255618,"about_ca_system_score_gemma":0.000076125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009121174,"about_ca_topic_score_gemma":0.000003412858,"domain_scores_codex":[0.9985597,0.000160749,0.0004009694,0.0003390659,0.00029716,0.0002423707],"domain_scores_gemma":[0.9989622,0.0002091367,0.0001152437,0.0005623109,0.00007701617,0.00007407234],"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":[0.00004336799,0.0003027669,0.00007590905,0.0001785285,0.00003848372,0.000003113359,0.0008775766,0.618765,0.0002256107,0.001560639,0.0004370749,0.377492],"study_design_scores_gemma":[0.0004363283,0.00004201409,0.0000495623,0.0001276802,0.00003237504,0.000005105646,0.002153315,0.98284,0.00003427603,0.0003895468,0.01361618,0.0002735987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005213366,0.004410445,0.8981119,0.001460413,0.00002851975,0.002192616,0.0001282947,0.000675189,0.08777927],"genre_scores_gemma":[0.9883755,0.000110087,0.00859677,0.0002140265,0.00001421951,0.002029668,0.0002654174,0.00003959118,0.0003546891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9831622,"threshold_uncertainty_score":0.9690812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02182270003081152,"score_gpt":0.2471613363672647,"score_spread":0.2253386363364532,"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."}}