{"id":"W3012491556","doi":"10.23919/fusion43075.2019.9011342","title":"A Machine Learning Task Selection Method for Radar Resource Management (Poster)","year":2019,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Reinforcement learning; Scheduling (production processes); Schedule; Radar; Task (project management); Artificial intelligence; Real-time computing; Machine learning; Engineering; Operations management","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":[0.0001931861,0.00008574323,0.00009037875,0.00008113441,0.00004348786,0.00003061639,0.00005454764,0.00004002421,0.0001583714],"category_scores_gemma":[0.000006818805,0.00008278991,0.00003859491,0.0001419634,0.000001882653,0.00004440913,0.00001333897,0.00009100664,0.00006949101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003089389,"about_ca_system_score_gemma":0.000001749745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006280251,"about_ca_topic_score_gemma":0.000001698298,"domain_scores_codex":[0.9995188,0.00001922183,0.0001119078,0.0001318475,0.00007739416,0.000140888],"domain_scores_gemma":[0.9998121,0.00004439149,0.0000150519,0.00007905204,0.00001814507,0.00003130678],"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":[0.00001458539,0.000007730542,0.0002010177,0.00008453014,0.00006293686,3.362302e-7,0.0001195416,0.9628744,0.0005450296,0.0006222673,0.0004838389,0.03498381],"study_design_scores_gemma":[0.0004686468,0.00003550529,0.00004087548,0.000009052863,0.00001497172,0.000003233141,0.00009812679,0.939561,0.001294909,0.00002452089,0.05833911,0.0001100453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008198878,0.00007878403,0.9692076,0.00005957641,0.0001506564,0.0002558527,0.000001469088,0.0005692063,0.02885689],"genre_scores_gemma":[0.01832966,0.00001511206,0.9645916,0.000112925,0.00005807697,0.00002573896,0.00003816676,0.00004221592,0.01678647],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05785527,"threshold_uncertainty_score":0.3376074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006635233163548508,"score_gpt":0.229452516611795,"score_spread":0.2228172834482465,"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."}}