{"id":"W2381421067","doi":"","title":"Design and Realization of an Intelligent Access Control System Based on DM642","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"IoT-based Smart Home Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Facial recognition system; Biometrics; Access control; Embedded system; Identification (biology); Software; Realization (probability); Reliability (semiconductor); Mobile phone; Control (management); Phone; Computer hardware; Human–computer interaction; Computer security; Artificial intelligence; Feature extraction; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002097371,0.000405895,0.0002983834,0.00049091,0.0003173717,0.0005635308,0.0006328769,0.0004081145,0.002890857],"category_scores_gemma":[0.0002501949,0.0001713587,0.0001935059,0.0001859534,0.0001738602,0.0003428609,0.0002663392,0.0002591363,0.0009066879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003128884,"about_ca_system_score_gemma":0.000366559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000772681,"about_ca_topic_score_gemma":0.0004882693,"domain_scores_codex":[0.999757,0.0000306976,0.00001627231,0.00004757632,0.0001061625,0.00004222459],"domain_scores_gemma":[0.9998803,0.0000143272,0.00001570721,0.00001508971,0.00005716978,0.0000173083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006827518,0.0002222297,0.003517934,0.0003355559,0.00008291852,0.0009082277,0.0003563791,0.007277719,0.7379275,0.01607496,0.005491334,0.2271224],"study_design_scores_gemma":[0.0003680273,0.001921888,0.008570755,0.00005997673,0.0001674483,0.002420179,0.0001096225,0.1488264,0.7388715,0.002282762,0.09628247,0.0001190075],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2538383,0.0006159282,0.7019727,0.0005506303,0.0003516541,0.0006694961,0.0002921409,0.005311029,0.03639814],"genre_scores_gemma":[0.8629587,0.0001966014,0.1218328,0.0002063796,0.0000691332,0.0002734589,0.0002310152,0.00006273004,0.01416919],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002890857,"threshold_uncertainty_score":0.009670854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540225045724911,"score_gpt":0.2236017805613808,"score_spread":0.2081995301041317,"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."}}