{"id":"W2625236330","doi":"10.1049/el.2017.1454","title":"Automated stationary human target detector for 3D through‐wall radar imagery","year":2017,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Clutter; Thresholding; Artificial intelligence; Computer vision; Computer science; Radar; Segmentation; Radar imaging; Detector; Visualization; Pattern recognition (psychology); Remote sensing; Image (mathematics); Geology; Telecommunications","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":[0.0002886318,0.0003542165,0.0004003095,0.0008798225,0.0002249903,0.0005252573,0.0004562626,0.0004725888,0.001720583],"category_scores_gemma":[0.0004834233,0.0002865709,0.0003655783,0.0003335537,0.000292487,0.0003102695,0.0004003647,0.0003918583,0.001294155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001831206,"about_ca_system_score_gemma":0.0003861615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006695192,"about_ca_topic_score_gemma":0.001786728,"domain_scores_codex":[0.999786,0.00005394201,0.000005562855,0.00003938578,0.00008836568,0.00002686272],"domain_scores_gemma":[0.9997775,0.00008281145,0.00002759325,0.00004512404,0.00005358786,0.00001325027],"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":[0.0002247594,0.00007685927,0.001450823,0.0001234176,0.00005633396,0.0002661914,0.0001746941,0.02680668,0.5511876,0.00322183,0.003921736,0.4124891],"study_design_scores_gemma":[0.00003256401,0.0001662667,0.008765797,0.00002269993,0.00003420284,0.001139054,0.00011024,0.7795671,0.1966465,0.002359074,0.01109907,0.00005752476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04468725,0.0002090421,0.9519364,0.00008338325,0.00002913515,0.0000373056,0.00009416168,0.001116647,0.001806592],"genre_scores_gemma":[0.2282258,0.0003216204,0.7677253,0.0000704163,0.00002537371,0.00005590616,0.0003989304,0.0001222358,0.00305431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001720583,"threshold_uncertainty_score":0.005755901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0090266669979462,"score_gpt":0.2468799363017217,"score_spread":0.2378532693037755,"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."}}