{"id":"W4289792774","doi":"10.1109/tgrs.2022.3196407","title":"Geometric Clutter Analysis for Airborne Passive Coherent Location Radar","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Game Technology (Canada)","funders":"","keywords":"Bistatic radar; Clutter; Radar horizon; Computer science; Remote sensing; Moving target indication; Cartesian coordinate system; Synthetic aperture radar; Passive radar; Radar; Continuous-wave radar; Radar imaging; Computer vision; Geology; Mathematics; Geometry; Telecommunications","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":[0.0003826441,0.0007464212,0.000393864,0.001303455,0.0002067814,0.0007735743,0.000495759,0.0003214313,0.001339071],"category_scores_gemma":[0.002145974,0.0002140555,0.0004830822,0.00152637,0.0005753891,0.001151691,0.0007827344,0.0004066665,0.0005458744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004899454,"about_ca_system_score_gemma":0.0002974297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006355492,"about_ca_topic_score_gemma":0.0004231165,"domain_scores_codex":[0.9994532,0.0001183116,0.00001711763,0.00006337836,0.0002931228,0.00005480197],"domain_scores_gemma":[0.9994383,0.000238803,0.00008916372,0.00006254121,0.000155222,0.00001590652],"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":[0.00007190654,0.00003263889,0.002074833,0.000152563,0.00005013848,0.0003192427,0.0001047686,0.7112198,0.02433929,0.1066728,0.001895489,0.1530665],"study_design_scores_gemma":[0.000006270725,0.00007759689,0.002130399,0.00001714631,0.00001540939,0.0005960414,0.00004523598,0.939953,0.00626799,0.04746257,0.003406117,0.0000223482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01777305,0.0006267417,0.9792128,0.0000794506,0.00002693099,0.000009937592,0.00005150844,0.0001542424,0.002065473],"genre_scores_gemma":[0.7589175,0.003593628,0.2315519,0.000228784,0.0003045046,0.00007036336,0.0007284574,0.0002264607,0.004378369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001339071,"threshold_uncertainty_score":0.004479647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284415910569116,"score_gpt":0.2195080195991186,"score_spread":0.2066638604934274,"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."}}