{"id":"W4306818976","doi":"10.1145/3510454.3516858","title":"HUDD","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Fonds National de la Recherche Luxembourg; European Commission","keywords":"Computer science","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":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003756879,0.0001775058,0.0001953366,0.0001230661,0.0001664697,0.00019426,0.00313354,0.0001062907,0.002380332],"category_scores_gemma":[0.00008232992,0.0001795172,0.0001139208,0.0001817469,0.000018496,0.0001281167,0.01587964,0.001395551,0.00008512746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008984724,"about_ca_system_score_gemma":0.0001709215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001121273,"about_ca_topic_score_gemma":0.000002375773,"domain_scores_codex":[0.9983988,0.0001507073,0.0001872319,0.0006350795,0.0004077893,0.0002203293],"domain_scores_gemma":[0.9983994,0.00008874435,0.0001303538,0.001297513,0.00002776298,0.0000562422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000173905,0.00003034947,0.0005810697,0.00003578631,0.00003475641,0.00006955052,0.0005626745,0.5337729,0.000007320273,0.4317619,0.008355457,0.02478647],"study_design_scores_gemma":[0.0001831834,0.00003039527,0.0007531064,0.00001622936,0.00001069909,0.00001101674,0.00006056111,0.81043,0.00002788334,0.06460749,0.1232327,0.0006367697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002583766,0.00006423524,0.8547391,0.001450989,0.002741378,0.0001202263,0.000001072786,0.0006675,0.1399571],"genre_scores_gemma":[0.1649116,0.00001701678,0.8171296,0.001174271,0.0003865315,0.00009700173,0.00002503166,0.00003677847,0.01622218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3671544,"threshold_uncertainty_score":0.9985316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02092173177462929,"score_gpt":0.2903201896606873,"score_spread":0.269398457886058,"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."}}