{"id":"W2077742958","doi":"10.1109/mis.2009.108","title":"Adversarial Knowledge Discovery","year":2009,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Adversarial system; Computer science; Normality; Face (sociological concept); Artificial intelligence; Data science; Knowledge extraction; Machine learning","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":[0.008459066,0.001186292,0.001613885,0.001863381,0.00125846,0.003541947,0.003197456,0.002603647,0.003415458],"category_scores_gemma":[0.03490296,0.0007185387,0.001005388,0.001699263,0.00439896,0.005989941,0.006923079,0.004710522,0.001298369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001519685,"about_ca_system_score_gemma":0.001992385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001052148,"about_ca_topic_score_gemma":0.0008420857,"domain_scores_codex":[0.9921017,0.002884838,0.0003707872,0.001587117,0.002511626,0.0005439543],"domain_scores_gemma":[0.970949,0.02088037,0.001310154,0.004928466,0.001470369,0.0004617688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002298311,0.0001578204,0.001886222,0.0003437214,0.0002869098,0.0003871986,0.0002446981,0.3447088,0.003321602,0.4908971,0.01379909,0.143737],"study_design_scores_gemma":[0.00001962595,0.00006440504,0.0002286737,0.00006605843,0.00003725274,0.000278766,0.0000534962,0.6235712,0.002477302,0.3644762,0.008695029,0.0000320669],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005338178,0.0006247575,0.9844398,0.001814722,0.0001297545,0.0001162912,0.0001733816,0.0003055997,0.007057492],"genre_scores_gemma":[0.7116337,0.002563111,0.2688145,0.001998283,0.000771721,0.0004661164,0.001058062,0.0002084522,0.01248609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008459066,"threshold_uncertainty_score":0.04473633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210910896871352,"score_gpt":0.2880726922928005,"score_spread":0.265963583324087,"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."}}