{"id":"W3185551961","doi":"10.1109/jsen.2021.3061178","title":"Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Infection Control and Ventilation","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Commission of Shanghai Municipality; Shanghai Education Development Foundation; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Computer science; Mobile phone; Phone; Face detection; Face (sociological concept); Coronavirus disease 2019 (COVID-19); Face masks; Artificial intelligence; Precision and recall; Computer vision; Facial recognition system; Feature extraction; Real-time computing; Pattern recognition (psychology); Telecommunications; Medicine; Infectious disease (medical specialty); Disease","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":[0.000339413,0.0001545101,0.0002975108,0.0001781646,0.0001612122,0.00003870261,0.00004150885,0.0001265618,0.0007695612],"category_scores_gemma":[0.00007510091,0.000139098,0.0001906009,0.0003891945,0.00001982618,0.0001025985,0.000006434353,0.0004694978,0.00007833951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001484674,"about_ca_system_score_gemma":0.0001484822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002073068,"about_ca_topic_score_gemma":0.00003787936,"domain_scores_codex":[0.998542,0.0001567121,0.0004158753,0.0001930562,0.0004729262,0.0002194926],"domain_scores_gemma":[0.9987756,0.00008200955,0.0002958499,0.0002208408,0.0004786316,0.0001470762],"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":[0.001235313,0.0004574934,0.001759479,0.0001764828,0.0001394028,0.0002774355,0.000634168,0.1271704,0.8621373,0.000004253626,0.0002502843,0.00575791],"study_design_scores_gemma":[0.005429274,0.0006917131,0.01625623,0.0002939295,0.0002075522,0.0007301263,0.001459242,0.1735021,0.7889793,0.00001227553,0.01220105,0.0002371561],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9169916,0.00008716978,0.07937757,0.0005214243,0.001177897,0.0002013178,0.00002387375,0.0000426546,0.001576511],"genre_scores_gemma":[0.9952924,0.00003291751,0.0002322338,0.0004728773,0.0003881564,0.000004357657,0.000008330205,0.00002506523,0.003543725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07914533,"threshold_uncertainty_score":0.8426155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01631937120421493,"score_gpt":0.270142404336231,"score_spread":0.2538230331320161,"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."}}