{"id":"W4287728232","doi":"10.1103/physrevd.107.016002","title":"Variational autoencoders for anomalous jet tagging","year":2023,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Anomaly detection; Outlier; Jet (fluid); Computer science; Anomaly (physics); Regularization (linguistics); Artificial intelligence; Pattern recognition (psychology); Physics; Particle physics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005764572,0.0003867078,0.0009019303,0.00008566164,0.0003189577,0.00008840183,0.001197133,0.00003949496,0.00001297001],"category_scores_gemma":[0.0003417531,0.0003226738,0.0008266756,0.002045704,0.00007532843,0.0004418558,0.0003377726,0.0002898343,0.0009644443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008538306,"about_ca_system_score_gemma":0.0001182686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007668536,"about_ca_topic_score_gemma":5.069178e-7,"domain_scores_codex":[0.9972224,0.000133001,0.0005777502,0.0008926966,0.0005881501,0.0005859731],"domain_scores_gemma":[0.9975771,0.0006122287,0.000331528,0.0009805148,0.0002417159,0.0002569267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003459412,0.0004669426,0.0001172019,0.003860422,0.0000682029,0.000004481637,0.00006171428,0.00004938187,0.004185557,0.7601345,0.1397638,0.09128439],"study_design_scores_gemma":[0.0002366286,0.0002299008,0.001013661,0.002806994,0.0002145098,0.000009031848,0.000002683669,0.2368204,0.002498164,0.2464642,0.5089353,0.0007685794],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009581454,0.015059,0.9080871,0.04853613,0.0005013482,0.006872343,0.0001314125,0.003964464,0.00726669],"genre_scores_gemma":[0.6034517,0.2082502,0.1084953,0.05407929,0.005324143,0.01824447,0.0005285533,0.0003288478,0.00129757],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7995919,"threshold_uncertainty_score":0.9999225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378164081759547,"score_gpt":0.4251199105942585,"score_spread":0.401338269776663,"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."}}