{"id":"W1973988288","doi":"10.1109/igarss.2008.4778780","title":"Radarsat-2 Moving Object Detection Experiment (MODEX)","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Moving target indication; Computer science; Object detection; Mode (computer interface); Object (grammar); Synthetic aperture radar; Computer vision; Artificial intelligence; Remote sensing; Real-time computing; Pattern recognition (psychology); Radar imaging; Radar; Geography; Telecommunications; Human–computer interaction","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":[0.00003240379,0.0001062306,0.00008751221,0.00006483607,0.00006598984,0.000007549572,0.00007419662,0.00003387099,0.00004207458],"category_scores_gemma":[0.000008648021,0.0001091622,0.00003315334,0.00008544771,0.00002123877,0.0001708806,0.00002261792,0.00009603233,0.00003033546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001278363,"about_ca_system_score_gemma":0.000004866231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003128943,"about_ca_topic_score_gemma":0.000004857939,"domain_scores_codex":[0.9995009,0.000006469649,0.0001099818,0.0001148951,0.0000989198,0.0001688361],"domain_scores_gemma":[0.99974,0.00001256215,0.000009296648,0.0001903633,0.00001312665,0.00003464592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007884243,0.00002696789,0.0002817861,0.00002450583,0.00002968765,0.0000428664,0.001005423,0.007711684,0.8848432,0.0002252029,0.003152973,0.1026478],"study_design_scores_gemma":[0.0000809286,0.00001326878,0.0001618577,0.00000615423,0.000001585336,0.00004788983,0.00003034705,0.02849182,0.9611978,0.0003072374,0.009511227,0.0001498719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1051764,0.0002570857,0.875871,0.00001012146,0.0001446648,0.00009857675,3.991789e-7,0.003512093,0.01492968],"genre_scores_gemma":[0.8462607,0.00005642078,0.1533844,0.00002867395,0.00004230445,0.00001017851,6.616022e-7,0.00003142418,0.0001852077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7410844,"threshold_uncertainty_score":0.4451506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124927202220009,"score_gpt":0.2296287777735021,"score_spread":0.2171360575515012,"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."}}