{"id":"W1968374676","doi":"10.1117/12.2065976","title":"Experimental 3-D SAR human target signature analysis","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Synthetic aperture radar; Computer science; Inverse synthetic aperture radar; Signature (topology); Radar imaging; Visualization; Artificial intelligence; Computer vision; Radar; Remote sensing; Geology; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000271552,0.0003073469,0.0001767546,0.0002405103,0.0002161548,0.0002279001,0.0002625984,0.0004177924,0.003922809],"category_scores_gemma":[0.0003976065,0.0001651242,0.0001436118,0.0003451341,0.0003051161,0.0002141688,0.0002862192,0.0002317302,0.0007544153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190656,"about_ca_system_score_gemma":0.0002042769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001170062,"about_ca_topic_score_gemma":0.001822941,"domain_scores_codex":[0.9998216,0.00002427894,0.00001049561,0.00003534903,0.00008015576,0.00002804832],"domain_scores_gemma":[0.9997507,0.00005230199,0.00002585921,0.00005786255,0.00008822548,0.00002516384],"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":[0.0009693142,0.0004988272,0.005321702,0.0001747484,0.00003622842,0.0006053002,0.0006649488,0.02260712,0.9321944,0.001143055,0.002338398,0.033446],"study_design_scores_gemma":[0.0001472509,0.003678877,0.05647666,0.00003364121,0.00006224627,0.00201658,0.001009468,0.07652284,0.8455949,0.0008822458,0.01345006,0.0001250702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9534432,0.00004488571,0.03816925,0.00006143562,0.00002773783,0.000132088,0.001155265,0.0003188025,0.006647367],"genre_scores_gemma":[0.9698673,0.00008289639,0.02452191,0.00006983653,0.000008021406,0.00008635609,0.0009551111,0.00006011679,0.004348482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003922809,"threshold_uncertainty_score":0.01312315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007008498624246885,"score_gpt":0.2271554158246013,"score_spread":0.2201469172003544,"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."}}