{"id":"W4392954954","doi":"10.1049/rsn2.12556","title":"Radar active oppressive interference suppression based on generative adversarial network","year":2024,"lang":"en","type":"article","venue":"IET Radar Sonar & Navigation","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Interference (communication); Computer science; Noise (video); Radar; SIGNAL (programming language); Algorithm; Echo (communications protocol); Electronic engineering; Artificial intelligence; Speech recognition; Telecommunications; Engineering; Channel (broadcasting)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003459882,0.0002416873,0.000195458,0.000117825,0.0003070296,0.0002786385,0.0002958904,0.0001585985,0.001546825],"category_scores_gemma":[0.00003583814,0.0001951008,0.0000879282,0.0004019612,0.0001306353,0.0004636912,0.0000288956,0.0005625668,0.000415787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006226306,"about_ca_system_score_gemma":0.0002727362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004901487,"about_ca_topic_score_gemma":0.0001494035,"domain_scores_codex":[0.9976515,0.0003358163,0.0002462025,0.0005662887,0.00073225,0.0004679793],"domain_scores_gemma":[0.9988948,0.000534523,0.0000663504,0.000233611,0.00009643226,0.000174279],"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":[0.002199813,0.0001139869,0.002817886,0.0001969534,0.0001976137,0.0006490721,0.003411508,0.5545894,0.01009873,0.0002118112,0.03923913,0.3862741],"study_design_scores_gemma":[0.0005269391,0.0005334984,0.003247553,0.000512018,0.00003642485,0.00001120562,0.0001599686,0.9668595,0.01721846,0.002891043,0.007677879,0.0003254839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3091649,0.001723425,0.6427993,0.005300142,0.01012462,0.003293833,0.003256801,0.0009909146,0.02334606],"genre_scores_gemma":[0.9874192,0.00002732638,0.009503886,0.000155609,0.0009489232,0.000006818098,0.001629709,0.00001433199,0.0002941529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6782544,"threshold_uncertainty_score":0.9993659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02127404352899737,"score_gpt":0.271274155381553,"score_spread":0.2500001118525556,"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."}}