{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001591339,0.000206758,0.000222054,0.00007668244,0.0005051948,0.0001995222,0.000348582,0.00005027491,0.00003429761],"category_scores_gemma":[0.00002440009,0.0002294489,0.0001350125,0.00004927959,0.0000588861,0.000251044,0.00002503803,0.0002027974,0.00002847006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000180304,"about_ca_system_score_gemma":0.0000223588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003530064,"about_ca_topic_score_gemma":0.00001456033,"domain_scores_codex":[0.998831,0.00001849403,0.0002284781,0.0002528767,0.000127104,0.0005420625],"domain_scores_gemma":[0.9992623,0.00003909017,0.00007745656,0.0005451142,0.00002921386,0.00004685486],"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":[0.000006021042,0.0000122408,0.0002868927,0.00007317978,0.0002337709,0.000009776447,0.0001820162,0.005079318,0.9172422,0.00004930712,0.07609028,0.0007349768],"study_design_scores_gemma":[0.001783951,0.0001063073,0.004272324,0.0000760423,0.0002355932,0.00002855109,0.00002215334,0.6000058,0.2069821,0.001132205,0.1839876,0.001367392],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8193232,0.001203817,0.1709713,0.00460687,0.0004055498,0.0003320484,0.0001106574,0.002428848,0.0006176988],"genre_scores_gemma":[0.9552875,0.0000423852,0.04285337,0.001105942,0.0001944164,0.00005149565,0.0001874222,0.0001051396,0.0001723437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7102602,"threshold_uncertainty_score":0.9356655,"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."}}