{"id":"W3021281145","doi":"10.1109/radar41533.2019.171271","title":"Surveillance RADAR development using frequency diversity to mitigate range eclipsing","year":2019,"lang":"en","type":"article","venue":"","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SR Research (Canada)","funders":"","keywords":"Radar; Waveform; Remote sensing; Continuous-wave radar; Computer science; Transceiver; Fire-control radar; Diversity scheme; Radar lock-on; Pulse-Doppler radar; Radar engineering details; SIGNAL (programming language); 3D radar; Bistatic radar; Radar systems; Low probability of intercept radar; Interference (communication); Radar imaging; Telecommunications; Fading; Geography; Wireless","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002699762,0.0002140871,0.0001734945,0.0002720648,0.00009762245,0.0002704513,0.0002641013,0.0002818298,0.000533068],"category_scores_gemma":[0.0003965837,0.00009033215,0.0001374579,0.0001966557,0.0001123276,0.0002969769,0.0002388984,0.0002183703,0.0002301718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000131512,"about_ca_system_score_gemma":0.0001951111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000145471,"about_ca_topic_score_gemma":0.0002063432,"domain_scores_codex":[0.9998032,0.00003669514,0.000008566807,0.00003677958,0.00009105802,0.00002363473],"domain_scores_gemma":[0.9996841,0.00005279244,0.00005654595,0.00002865825,0.0001580226,0.00001998169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001162382,0.00006659598,0.002454228,0.0001124141,0.00003088542,0.000245898,0.0001267375,0.01029899,0.8657687,0.002275348,0.0005784456,0.1179256],"study_design_scores_gemma":[0.00006525996,0.001889014,0.009118152,0.00004953238,0.00008993968,0.002105996,0.00009814091,0.1288047,0.8399412,0.0009516993,0.0168465,0.00003977913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4787485,0.0008583723,0.5089329,0.0002332607,0.00009489294,0.00008817772,0.00005899475,0.0006451852,0.01033967],"genre_scores_gemma":[0.8867404,0.0004281889,0.1094502,0.00008960983,0.00004676207,0.0000373533,0.00008206029,0.00002136649,0.00310404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000533068,"threshold_uncertainty_score":0.001783311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01626510617324084,"score_gpt":0.2008789090826985,"score_spread":0.1846138029094576,"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."}}