{"id":"W1521437980","doi":"10.1109/acssc.1999.831903","title":"Hierarchical ship classifier for airborne synthetic aperture radar (SAR) images","year":2003,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"Michael and Susan Dell Foundation","keywords":"Synthetic aperture radar; Classifier (UML); Artificial intelligence; Testbed; Computer science; Computer vision; Radar; Sensor fusion; Automatic target recognition; Combatant; Inverse synthetic aperture radar; Radar imaging; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004149063,0.000200962,0.0002151535,0.00007415888,0.0003798492,0.0001739128,0.0007748217,0.0001545062,0.0001878958],"category_scores_gemma":[0.0004195044,0.0001525926,0.0001317169,0.0002572233,0.00009032264,0.0002624301,0.0001533858,0.0002743442,0.00008638569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001960121,"about_ca_system_score_gemma":0.00005651639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003833794,"about_ca_topic_score_gemma":0.000001936841,"domain_scores_codex":[0.9982408,0.0001315808,0.0002626842,0.0006044404,0.0002688634,0.0004916426],"domain_scores_gemma":[0.9977654,0.001073067,0.00005064965,0.0008532234,0.00007107123,0.0001866255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002051718,0.0001544435,0.0001191347,0.0000242831,0.00002305923,0.0000220671,0.0001685259,0.00005378507,0.0007895086,0.8161756,0.1332734,0.04917575],"study_design_scores_gemma":[0.0005548123,0.00009841474,0.0003545646,0.00002734122,0.000009990303,0.00006658736,0.00002414482,0.02041089,0.00259511,0.01928441,0.9561914,0.0003823272],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005002961,0.0002644118,0.9758133,0.00424435,0.0007847083,0.0002260975,0.00001883411,0.0003751178,0.01777285],"genre_scores_gemma":[0.3566516,0.00004563749,0.6342101,0.003431075,0.0001891176,0.00002264941,0.00001914938,0.00002968847,0.005401052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8229181,"threshold_uncertainty_score":0.6222547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02083320827389423,"score_gpt":0.2478997212154082,"score_spread":0.227066512941514,"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."}}