{"id":"W2131476315","doi":"10.1109/taes.2011.5937266","title":"Detection Performance using Frequency Diversity with Distributed Sensors","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Antenna diversity; Diversity scheme; Interference (communication); Computer science; Space-time adaptive processing; Context (archaeology); Radar; Diversity combining; Signal-to-noise ratio (imaging); Diversity gain; Electronic engineering; Radar engineering details; Telecommunications; Radar imaging; Fading; Engineering; Geography","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":[0.0001032224,0.0001799851,0.0001838241,0.00007498825,0.0004633435,0.00003912187,0.00006006124,0.00009179563,0.000006198819],"category_scores_gemma":[3.332612e-7,0.000158045,0.00003243622,0.0002212508,0.00003375174,0.0002049575,7.530036e-7,0.0002516281,0.000005750652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002161318,"about_ca_system_score_gemma":0.00002437648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006823509,"about_ca_topic_score_gemma":0.0002129254,"domain_scores_codex":[0.9991405,0.0000245847,0.0001444445,0.0001860535,0.000147994,0.0003564301],"domain_scores_gemma":[0.9997063,0.00001028205,0.00003948347,0.0001327682,0.00003653041,0.00007467141],"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":[0.0004055039,0.0001830118,0.004686543,0.001597464,0.0009167268,0.00003698502,0.006475228,0.8583782,0.1168693,0.0001188473,0.00002823037,0.01030391],"study_design_scores_gemma":[0.001801571,0.001334231,0.001768737,0.0006978479,0.0003095982,0.0007940186,0.002288648,0.8196422,0.1698643,0.00002222427,0.0001790314,0.001297596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6259819,0.0002634534,0.3731519,0.000001219382,0.0002006236,0.0001186303,0.00000540299,0.0001263054,0.0001506053],"genre_scores_gemma":[0.9997101,0.0001066159,0.00005397206,0.000002296886,0.00003136875,0.00001098171,5.682464e-7,0.00002676253,0.0000573764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3737282,"threshold_uncertainty_score":0.6444888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537665519055374,"score_gpt":0.1740500314109947,"score_spread":0.1586733762204409,"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."}}