{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007466893,0.0001941181,0.0001986935,0.001407213,0.000821989,0.001967043,0.002336096,0.0001073765,0.00001307534],"category_scores_gemma":[0.0001647308,0.0001948473,0.0001323375,0.001323613,0.0002166567,0.001073136,0.0002010723,0.000188706,0.0003817163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005817643,"about_ca_system_score_gemma":0.00002276953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000669996,"about_ca_topic_score_gemma":0.000003372046,"domain_scores_codex":[0.9977885,0.0001144365,0.0003998747,0.0006467095,0.0006533919,0.0003970386],"domain_scores_gemma":[0.9974602,0.0001091567,0.0004327579,0.001752356,0.000108206,0.0001372679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001122923,0.0002625538,0.00604283,0.00008125399,0.0001991344,0.00009490271,0.002999621,0.00001882719,0.7552138,0.004572592,0.05068889,0.1798143],"study_design_scores_gemma":[0.001317802,0.000049028,0.5126036,0.00005031843,0.00007735143,0.00006322098,0.0001464694,0.02455393,0.4306015,0.0005467505,0.02858752,0.00140251],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9322817,0.0000575748,0.05749046,0.006584503,0.002955746,0.0001927601,0.00002271203,0.0001647739,0.0002497817],"genre_scores_gemma":[0.9943829,0.00002778725,0.003804972,0.001084589,0.0001867094,0.000008473948,0.00001338859,0.00001491452,0.0004763121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5065607,"threshold_uncertainty_score":0.999069,"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."}}