{"id":"W3099026386","doi":"10.1101/366724","title":"Attack and defence in cellular decision-making: lessons from machine learning","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Samsung; Samsung Advanced Institute of Technology; McGill University","keywords":"Artificial intelligence; Computer science; Decision boundary; Machine learning; Analogy; Artificial neural network; Adversarial system; Support vector machine","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001429862,0.0007897598,0.0008542867,0.0005631724,0.0003862892,0.0007685179,0.002509267,0.0007096382,0.00005251106],"category_scores_gemma":[0.001789151,0.0008809433,0.0001387167,0.0009408958,0.0002364501,0.0005602125,0.004926214,0.002496239,0.00009451994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003218202,"about_ca_system_score_gemma":0.00045619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002405283,"about_ca_topic_score_gemma":0.00002421386,"domain_scores_codex":[0.9948568,0.0005137229,0.0007910866,0.002280698,0.00072303,0.0008346002],"domain_scores_gemma":[0.9958016,0.0008284269,0.0006910693,0.00208184,0.0003086622,0.0002884132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003200824,0.0008239188,0.641969,0.000972971,0.0006925781,0.0032031,0.001020665,0.1497144,0.1828373,0.0168222,0.0005977029,0.001026084],"study_design_scores_gemma":[0.00129311,0.0001151406,0.1725514,0.003068662,0.00009835784,9.866956e-8,0.000007351833,0.8053223,0.01018412,0.0003189025,0.004451082,0.002589464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5087202,0.001530391,0.4874623,0.0002484254,0.001216593,0.0003180013,0.00002884229,0.0004626932,0.00001257092],"genre_scores_gemma":[0.8064539,0.000151191,0.1927467,0.0001188285,0.0003834122,0.00004198441,2.947176e-7,0.0001012951,0.000002318498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6556079,"threshold_uncertainty_score":0.999805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933415370169873,"score_gpt":0.2639332904317166,"score_spread":0.2445991367300179,"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."}}