{"id":"W2027420164","doi":"10.1117/12.2017057","title":"Textural feature based target detection in through-the-wall radar imagery","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Radar imaging; Remote sensing; Computer science; Feature (linguistics); Radar; Feature extraction; Artificial intelligence; Computer vision; Radar detection; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003072909,0.000305127,0.0003402529,0.00006533175,0.00008083585,0.0001231321,0.0007086557,0.0001856013,0.0000193857],"category_scores_gemma":[0.000230633,0.0002259713,0.0004721066,0.0004737167,0.0001632045,0.000571495,0.00007083942,0.0005093669,0.000005932582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001391181,"about_ca_system_score_gemma":0.00001416097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003887077,"about_ca_topic_score_gemma":4.130262e-7,"domain_scores_codex":[0.9983932,2.574225e-8,0.0004879305,0.0002909898,0.0004285095,0.0003993341],"domain_scores_gemma":[0.998831,0.0002038742,0.0001526062,0.00007468581,0.0006564145,0.00008135932],"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":[0.00001808829,0.00007663853,0.00009789332,0.0003299993,0.0001393752,4.092387e-8,0.0001418127,0.0009426671,0.9131259,0.08018983,0.003756928,0.00118083],"study_design_scores_gemma":[0.001621512,0.0002360903,0.01251293,0.0003616628,0.0001463406,0.00001279462,0.001316407,0.4335665,0.5143061,0.02014779,0.01489001,0.0008818381],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922941,0.0001028542,0.0008476978,0.004092895,0.0001842421,0.0007544667,0.00002090377,0.000143091,0.001559695],"genre_scores_gemma":[0.7329338,0.00003445336,0.2657595,0.0001785652,0.0003223309,0.0005800598,0.00000756118,0.00007208237,0.0001116184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4326238,"threshold_uncertainty_score":0.9214841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008622521661144442,"score_gpt":0.2234531711417563,"score_spread":0.2148306494806118,"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."}}