{"id":"W4400482476","doi":"10.1587/transele.2024dii0006","title":"Dielectric Lens-Based Millimeter Wave Imaging for Concealed Object Detection in Security Applications","year":2024,"lang":"en","type":"article","venue":"IEICE Transactions on Electronics","topic":"Terahertz technology and applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Aluminerie Alouette (Canada)","funders":"Ministry of Land, Infrastructure, Transport and Tourism","keywords":"Extremely high frequency; Dielectric; Lens (geology); Optics; Microwave imaging; Millimeter; Object detection; Computer science; Materials science; Physics; Optoelectronics; Artificial intelligence; Telecommunications; Microwave; Pattern recognition (psychology)","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":[0.0002164501,0.0004961546,0.0003243981,0.0003695197,0.0001384319,0.0006739927,0.0004819988,0.0005589499,0.001095884],"category_scores_gemma":[0.0003271645,0.0002238697,0.0002791385,0.0004287872,0.0003472063,0.0008193561,0.0005807351,0.0003588086,0.0007995783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002992187,"about_ca_system_score_gemma":0.0002858728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000288653,"about_ca_topic_score_gemma":0.0004934613,"domain_scores_codex":[0.9998062,0.00002919268,0.00000816251,0.00003837438,0.00009452776,0.00002366766],"domain_scores_gemma":[0.9997937,0.0000513712,0.00007136185,0.00003453197,0.00003579267,0.00001323483],"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":[0.00009517615,0.00002962998,0.0003802405,0.0001103013,0.00001035627,0.0001091826,0.00004918011,0.001165759,0.9654958,0.001496138,0.0002489396,0.03080932],"study_design_scores_gemma":[0.000009946293,0.000219489,0.001061894,0.00001204278,0.00001434248,0.0006429188,0.00004783952,0.03539601,0.9582995,0.0003208439,0.003951263,0.00002390529],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3388735,0.003175362,0.6469651,0.0002668655,0.0001261912,0.00009445126,0.0002406198,0.001123218,0.009134678],"genre_scores_gemma":[0.6882865,0.002633648,0.3020755,0.0001880252,0.00005178733,0.00005493697,0.0002365875,0.00009717786,0.006375804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001095884,"threshold_uncertainty_score":0.003666103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006474992158030243,"score_gpt":0.2254131892667375,"score_spread":0.2189381971087073,"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."}}