{"id":"W2163114957","doi":"10.1109/igarss.1989.577820","title":"Helicopter Radar Simulations From Fine Resolution Airborne Sar Imagery","year":2005,"lang":"en","type":"article","venue":"","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Transport Canada","keywords":"Remote sensing; Radar imaging; Synthetic aperture radar; Radar; Side looking airborne radar; Space-based radar; Inverse synthetic aperture radar; 3D radar; Early-warning radar; Radar lock-on; Image resolution; Bistatic radar; Geology; Computer science; Computer vision; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001882345,0.0000804699,0.00008574146,0.00006060784,0.00003734264,0.00001108189,0.00005918123,0.00007508074,0.0007937613],"category_scores_gemma":[0.00001341588,0.00007944416,0.00002883176,0.0001064495,0.0000159022,0.0001550866,0.00001620153,0.0000913448,0.000576107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004324365,"about_ca_system_score_gemma":0.000003901665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002522521,"about_ca_topic_score_gemma":0.0001364929,"domain_scores_codex":[0.9995921,0.000004418286,0.0001141363,0.00009878166,0.00005743355,0.0001330879],"domain_scores_gemma":[0.9997315,0.00003123867,0.00001291142,0.0001808261,0.00001755537,0.00002597084],"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":[0.00001474429,0.0001384814,0.00289717,0.00001810833,0.0001195174,0.000006304461,0.0005839922,0.5321357,0.1661099,0.005139249,0.1368529,0.1559839],"study_design_scores_gemma":[0.0006143252,0.00002325849,0.006894882,0.0000120183,0.00002277858,0.000001425759,0.00004293784,0.6949415,0.1305384,0.00146066,0.1651116,0.0003361734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.842755,0.0004388621,0.1447469,0.003700694,0.0001488985,0.0001203642,0.00005283635,0.001838103,0.006198321],"genre_scores_gemma":[0.9843215,0.00001768295,0.014156,0.0001281788,0.0002106415,0.00000199335,0.00005132845,0.00001603512,0.001096677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1628058,"threshold_uncertainty_score":0.869113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007348098843438038,"score_gpt":0.2076193734786283,"score_spread":0.2002712746351903,"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."}}