{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002504445,0.0004654224,0.0005153735,0.0004528195,0.0004835526,0.0007167913,0.0006298051,0.001322138,0.004881529],"category_scores_gemma":[0.001773668,0.0005427361,0.0005164482,0.000537037,0.0004215733,0.0006683755,0.0003508897,0.0006931663,0.0004631496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006008942,"about_ca_system_score_gemma":0.0005266417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03824877,"about_ca_topic_score_gemma":0.02706981,"domain_scores_codex":[0.9998884,0.00003170251,0.000007599812,0.00001921238,0.0000244273,0.00002861544],"domain_scores_gemma":[0.9990392,0.000619623,0.00007333451,0.00006481012,0.0001332178,0.00006973504],"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":[0.00007044398,0.00004402528,0.001145817,0.00002567747,0.00002086634,0.0001119467,0.00003644096,0.9951539,0.0008993496,0.0004311003,0.0006033349,0.001457083],"study_design_scores_gemma":[0.00003324542,0.00003268221,0.001241878,0.000005368154,0.000007105209,0.00002269196,0.00003146959,0.9978324,0.0003043279,0.0002626402,0.0002180643,0.000008226912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739999,0.0002368823,0.008020354,0.0004255603,0.00008141332,0.00006251124,0.002190252,0.000373855,0.01460924],"genre_scores_gemma":[0.9937657,0.0001076565,0.003302351,0.00007495043,0.00001576579,0.00002576524,0.0008895708,0.0001018706,0.001716309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03824877,"threshold_uncertainty_score":0.07605231,"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."}}