{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006610092,0.0003547039,0.0002928623,0.000261267,0.0002695113,0.0003191595,0.0004090985,0.0004706951,0.001574961],"category_scores_gemma":[0.000418968,0.0001747097,0.0001645203,0.0002545551,0.0001781422,0.0003791458,0.0004128054,0.0004965199,0.000524938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001952656,"about_ca_system_score_gemma":0.0001888336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006397671,"about_ca_topic_score_gemma":0.001250582,"domain_scores_codex":[0.9997831,0.00003283297,0.000006234018,0.00006455348,0.00008251014,0.00003081329],"domain_scores_gemma":[0.9997988,0.00005695116,0.00002052664,0.00004196609,0.00005686639,0.0000249735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003626429,0.0009120144,0.01641105,0.0003031309,0.0001919159,0.000345654,0.0003274718,0.01627373,0.7857541,0.005561929,0.00735424,0.1629384],"study_design_scores_gemma":[0.00125319,0.009353685,0.08400033,0.00004676178,0.0001483194,0.001163782,0.0001321099,0.07835757,0.7809781,0.002260213,0.04218178,0.0001241711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8785862,0.0003472149,0.09516361,0.0002531596,0.0001542547,0.0005541897,0.003004505,0.004839079,0.01709778],"genre_scores_gemma":[0.8466398,0.0002766711,0.1360044,0.0004446612,0.00007694705,0.0004362408,0.007471391,0.0002034587,0.008446533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001574961,"threshold_uncertainty_score":0.005268753,"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."}}