{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001350768,0.0005561758,0.0004092061,0.0008412348,0.000175733,0.0002888341,0.0003697438,0.0004052358,0.002002193],"category_scores_gemma":[0.0005503865,0.0001089555,0.0002330547,0.0002785696,0.00008905018,0.000357338,0.0003605751,0.0001730298,0.001108364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002053409,"about_ca_system_score_gemma":0.0002687238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002113176,"about_ca_topic_score_gemma":0.003407049,"domain_scores_codex":[0.9997472,0.00001739628,0.00001029654,0.00006926353,0.0001111181,0.00004476837],"domain_scores_gemma":[0.9997986,0.00002580398,0.0000325599,0.00002136591,0.00009535986,0.0000263703],"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":[0.001191662,0.000190203,0.04903612,0.0003644566,0.00007511453,0.001005672,0.0002694999,0.002051983,0.2909219,0.0004532849,0.0129474,0.6414927],"study_design_scores_gemma":[0.00006138372,0.001472061,0.2214904,0.00007325967,0.0001671716,0.006151241,0.0008171573,0.3800492,0.3698857,0.0007512465,0.01895388,0.0001272661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8300235,0.001524516,0.150178,0.0003859994,0.000421218,0.0003816209,0.002278804,0.007997137,0.006809177],"genre_scores_gemma":[0.9006883,0.0005173843,0.09125852,0.0001633538,0.00008522251,0.0001004994,0.001408097,0.00007104958,0.00570759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002113176,"threshold_uncertainty_score":0.006697953,"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."}}