{"id":"W3206880386","doi":"10.1145/3474085.3475591","title":"DAWN","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Digital watermarking; Adversary; Watermark; Embedding; Artificial intelligence; Adversarial system; Machine learning; Insider threat; Set (abstract data type); Computer security; Surrogate model; Data mining; Image (mathematics); Insider; Law","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007728042,0.00003747326,0.00004620241,0.00001537042,0.00005424088,0.00007614207,0.0003219632,0.00001757729,0.0002424041],"category_scores_gemma":[0.00009803553,0.00003438472,0.00002198587,0.0002127462,0.000009012112,0.0002147738,0.0003230565,0.0000774257,0.0001764438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009216365,"about_ca_system_score_gemma":0.00004858675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004530021,"about_ca_topic_score_gemma":0.000002262453,"domain_scores_codex":[0.9995328,0.00003428097,0.00005684092,0.0001676874,0.0001059007,0.0001024553],"domain_scores_gemma":[0.9995455,0.0000457089,0.00001309143,0.000326783,0.0000376241,0.00003130284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[2.230196e-7,0.00001093323,0.0007295308,0.000001617518,0.000004092591,0.00008440854,0.0001047393,0.002273027,0.0004688919,0.9578014,0.001410076,0.03711108],"study_design_scores_gemma":[0.0006041453,0.00002781903,0.006927407,0.00001293342,0.000005258678,0.0001778872,0.0001079404,0.7250185,0.01706771,0.04734714,0.2022396,0.0004637099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006932856,0.00002388702,0.8715356,0.001880542,0.0002759674,0.000009178466,2.803893e-8,0.0001623695,0.1254191],"genre_scores_gemma":[0.4298838,0.000001928889,0.5585973,0.00101937,0.00007305203,0.000001116665,6.909592e-7,0.000003723201,0.01041902],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9104542,"threshold_uncertainty_score":0.2654155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0088661727294761,"score_gpt":0.2420518776041695,"score_spread":0.2331857048746935,"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."}}