{"id":"W2591206973","doi":"10.1109/lsens.2017.2673551","title":"Wireless Biometric Individual Identification Utilizing Millimeter Waves","year":2017,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Google","keywords":"Biometrics; Computer science; Transmitter; Extremely high frequency; Radar; Identification (biology); Wireless; Authentication (law); Feature (linguistics); SIGNAL (programming language); Signal processing; Real-time computing; Artificial intelligence; Telecommunications; Computer security; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003661737,0.0002934705,0.0003454947,0.0005920369,0.0001820556,0.0006576809,0.0004160177,0.000711472,0.002024141],"category_scores_gemma":[0.0008424866,0.0001783946,0.0002319499,0.0009524965,0.0002406546,0.001010213,0.0008134453,0.0003643691,0.001812203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001809743,"about_ca_system_score_gemma":0.0001191754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002767868,"about_ca_topic_score_gemma":0.0003574881,"domain_scores_codex":[0.9994984,0.0001032186,0.00002193764,0.00008787309,0.000244343,0.00004425626],"domain_scores_gemma":[0.9996239,0.00009645207,0.00008151637,0.0001108062,0.00007490378,0.00001232281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002267798,0.00008874227,0.006245634,0.0003762628,0.0000632602,0.0002911749,0.0001892174,0.005760835,0.3272861,0.01298796,0.006276689,0.6402074],"study_design_scores_gemma":[0.00006476581,0.001033649,0.03583046,0.0003189211,0.0002198453,0.005428728,0.0003469702,0.2297153,0.5175458,0.01301537,0.1962577,0.0002224323],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08426075,0.004367844,0.8907233,0.0006939708,0.0004203955,0.00009707532,0.0004386276,0.001756898,0.01724106],"genre_scores_gemma":[0.6887833,0.00503807,0.2865348,0.0007936836,0.0003789854,0.0001702831,0.0005666065,0.00009341427,0.01764087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002024141,"threshold_uncertainty_score":0.006771445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04973186617853767,"score_gpt":0.2815374972757827,"score_spread":0.231805631097245,"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."}}