{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001704537,0.001326991,0.0006759334,0.001655649,0.0004309952,0.00197445,0.002994439,0.001186676,0.06094031],"category_scores_gemma":[0.008478522,0.0009090192,0.001022944,0.0005023288,0.0006985721,0.002641726,0.003556405,0.001695203,0.01722695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006847343,"about_ca_system_score_gemma":0.0009654735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878266,"about_ca_topic_score_gemma":0.002803165,"domain_scores_codex":[0.9987795,0.0001912663,0.00009206601,0.0003415809,0.0004878425,0.0001077905],"domain_scores_gemma":[0.9976472,0.0009259421,0.0001389551,0.0008447688,0.0003319017,0.0001112249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008729336,0.0002030516,0.007261132,0.001481359,0.0002297716,0.0007833599,0.0003947683,0.04494791,0.01245364,0.02766539,0.3775767,0.52613],"study_design_scores_gemma":[0.0003610083,0.0002721953,0.002039978,0.0002990481,0.00008862933,0.0009583939,0.0001194807,0.3563356,0.05572852,0.05481197,0.5288067,0.0001784629],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005569118,0.0006283622,0.5747869,0.0005955997,0.0005135181,0.0002151371,0.01070295,0.3901776,0.01681081],"genre_scores_gemma":[0.2160805,0.001067186,0.6271477,0.002002564,0.0002192325,0.0007758776,0.04644448,0.0585718,0.04769066],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9390597,"threshold_uncertainty_score":0,"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."}}