{"id":"W3155727414","doi":"10.3389/frobt.2021.612392","title":"FaceGuard: A Wearable System To Avoid Face Touching","year":2021,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Face recognition and analysis","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; New York University Abu Dhabi","keywords":"Computer science; Wearable computer; Face (sociological concept); Wearable technology; Human–computer interaction; Artificial intelligence; Computer vision; Embedded system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002375648,0.0008409607,0.0005352433,0.0003849875,0.0001790901,0.0003607446,0.001159899,0.0007032573,0.01052253],"category_scores_gemma":[0.0008506987,0.0003201389,0.0003264591,0.0001394421,0.0001531537,0.0007661398,0.0008460121,0.0003571017,0.002876932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002104016,"about_ca_system_score_gemma":0.0002523972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001175782,"about_ca_topic_score_gemma":0.001481331,"domain_scores_codex":[0.9997895,0.00001352363,0.00001286033,0.00008127447,0.00007756751,0.0000252696],"domain_scores_gemma":[0.9998196,0.00003830315,0.00003145429,0.00002932391,0.00006016503,0.00002119893],"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":[0.001922957,0.0003176199,0.005791003,0.0005617974,0.000127212,0.0007165868,0.0002725649,0.003036175,0.2973679,0.0007310269,0.04018623,0.6489689],"study_design_scores_gemma":[0.0004308852,0.004861046,0.07245212,0.0003517674,0.0005427201,0.008028506,0.0002412844,0.3920272,0.3881533,0.003197723,0.1292491,0.0004643755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2749831,0.002988344,0.6183024,0.0007256643,0.001303173,0.000900309,0.005631404,0.07344653,0.02171909],"genre_scores_gemma":[0.8249443,0.0009118851,0.1401369,0.001220196,0.0002053357,0.0005924628,0.003336234,0.0006438794,0.02800893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01052253,"threshold_uncertainty_score":0.03520143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007870094666945025,"score_gpt":0.2210610950026989,"score_spread":0.2131910003357539,"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."}}