{"id":"W4402905718","doi":"10.1167/jov.24.10.1349","title":"Physically blurred faces are more recognizable at a distance","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science","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":[0.0004943499,0.0002725662,0.0002470903,0.0004205692,0.0001706356,0.0005401397,0.000167007,0.0004279536,0.007559148],"category_scores_gemma":[0.002671974,0.0002239493,0.0002047447,0.00008500714,0.0002968563,0.0006810166,0.0005693389,0.0004204139,0.0005120002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001577371,"about_ca_system_score_gemma":0.00008916252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005243194,"about_ca_topic_score_gemma":0.0005023743,"domain_scores_codex":[0.9995731,0.00005431205,0.00002703333,0.0001460236,0.0001540659,0.000045472],"domain_scores_gemma":[0.9989385,0.0003008216,0.0003686847,0.0001025121,0.0001603345,0.0001291532],"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":[0.0008820132,0.0001062763,0.01044778,0.0001334133,0.00002764966,0.0002306593,0.0006478944,0.00007756425,0.9678794,0.0001937481,0.0001935043,0.01918016],"study_design_scores_gemma":[0.0000935592,0.003420524,0.8627862,0.0000563753,0.0001050507,0.002814983,0.0009940267,0.001048294,0.1251904,0.0007081153,0.002737396,0.00004491632],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970723,0.0003032611,0.0008629327,0.00004058231,0.0000194782,0.00001661017,0.00002888068,0.00002712261,0.001628798],"genre_scores_gemma":[0.9965681,0.0001463162,0.00169247,0.00007030365,0.00001408455,0.00001061451,0.00005675187,0.00001394709,0.001427343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007559148,"threshold_uncertainty_score":0.02528787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220024125915199,"score_gpt":0.2843008071740473,"score_spread":0.2721005659148953,"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."}}