{"id":"W2127711499","doi":"10.1109/icpp.1996.537391","title":"Parallel processors for synthetic aperture radar imaging","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Synthetic aperture radar; Digital signal processing; Signal processing; Digital signal processor; Flexibility (engineering); Computation; Radar imaging; Computer hardware; Inverse synthetic aperture radar; Computer architecture; Parallel processing; Radar; Artificial intelligence; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000390741,0.0001368025,0.0001162151,0.00005204034,0.00004334026,0.00002339136,0.0001455362,0.00003015216,0.0001205925],"category_scores_gemma":[0.00006115036,0.000125106,0.00004599425,0.00007715037,0.00002459002,0.0001742598,0.00001389994,0.00008986266,0.00003958082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003690972,"about_ca_system_score_gemma":0.000001535832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.265634e-7,"about_ca_topic_score_gemma":7.68595e-7,"domain_scores_codex":[0.9994031,0.000003284019,0.0001199297,0.0001497763,0.00007375301,0.0002501686],"domain_scores_gemma":[0.9996541,0.00007291535,0.00001211479,0.0001943665,0.00002454193,0.00004199465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002324151,0.0001769596,0.0006317176,0.00128812,0.0001318474,0.00004364716,0.001528239,0.01217226,0.05314755,0.01429894,0.5354847,0.3810728],"study_design_scores_gemma":[0.0002933609,0.0000117743,0.00001224767,0.00005220062,0.00001306861,0.00003120436,0.00004519205,0.6346238,0.01121204,0.00656788,0.3467224,0.0004147943],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001798258,0.00145509,0.9677894,0.000990519,0.00007316341,0.0003210242,0.000005805338,0.003285866,0.02589937],"genre_scores_gemma":[0.4217997,0.0001281311,0.5754215,0.0005243862,0.00006475583,0.0001667679,0.000004407252,0.0001016874,0.001788651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6224515,"threshold_uncertainty_score":0.5101674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137127184983637,"score_gpt":0.2244086181103279,"score_spread":0.2130373462604915,"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."}}