{"id":"W4310533687","doi":"10.1049/rsn2.12337","title":"Artificial intelligence meets radar resource management: A comprehensive background and literature review","year":2022,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Defence Research and Development Canada","funders":"Defence Research and Development Canada","keywords":"Computer science; Radar; Heuristics; Scheduling (production processes); Resource management (computing); Artificial intelligence; Resource allocation; Operations research; Machine learning; Systems engineering; Data science; Management science; Distributed computing; Engineering; Telecommunications; Operations management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001290081,0.001163606,0.000932732,0.004435653,0.0005191871,0.002467937,0.001202108,0.001878779,0.005109561],"category_scores_gemma":[0.003633436,0.0005299344,0.000750398,0.008432065,0.0007470064,0.003074078,0.000973823,0.001702894,0.001529579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009455839,"about_ca_system_score_gemma":0.002108645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002382408,"about_ca_topic_score_gemma":0.001774962,"domain_scores_codex":[0.9993914,0.0001668655,0.0000744277,0.0001187579,0.0001957314,0.00005270637],"domain_scores_gemma":[0.9956216,0.003503792,0.0001955555,0.00009145593,0.0005060578,0.00008146992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005258506,0.0001310154,0.0008985816,0.02365099,0.000139914,0.000265426,0.0002975376,0.005264367,0.000768435,0.04626837,0.02918742,0.8930753],"study_design_scores_gemma":[0.00001437154,0.0001798265,0.00319461,0.02492968,0.000268608,0.001030972,0.0006573736,0.008674059,0.0009458869,0.04875225,0.9112523,0.0001000914],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005896108,0.9866779,0.004719484,0.001549837,0.0003080363,0.000017541,0.00005202948,0.00003232155,0.006053363],"genre_scores_gemma":[0.007026921,0.9878246,0.003032173,0.0004731678,0.0007881848,0.00001983402,0.00009314116,0.00001071637,0.0007314155],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005109561,"threshold_uncertainty_score":0.01709312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02423958497639595,"score_gpt":0.2562146240587154,"score_spread":0.2319750390823195,"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."}}