{"id":"W2303509185","doi":"10.1093/mutage/gew011","title":"Genetic toxicology at the crossroads—from qualitative hazard evaluation to quantitative risk assessment","year":2016,"lang":"en","type":"article","venue":"Mutagenesis","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Canada; National Centre for the Replacement, Refinement and Reduction of Animals in Research","keywords":"Risk assessment; Hazard; Hazard analysis; Risk analysis (engineering); Computer science; Data science; Computational biology; Toxicology; Medicine; Biology; Engineering; Reliability engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008223221,0.0001856048,0.0001551802,0.00003633086,0.0003191115,0.00002111004,0.000299703,0.0001715891,0.000306723],"category_scores_gemma":[0.000284097,0.0001133987,0.0001148272,0.0001163992,0.0002064359,0.000003611667,0.0002905023,0.00005245667,0.0001233103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101306,"about_ca_system_score_gemma":0.0001057679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001328863,"about_ca_topic_score_gemma":0.0007441984,"domain_scores_codex":[0.9978611,0.0008074425,0.0002628136,0.0005874897,0.0001921819,0.0002889413],"domain_scores_gemma":[0.9987074,0.0001499927,0.0001581,0.0006766748,0.0002212815,0.00008654998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006085499,0.00004469032,0.00342551,0.000001195083,0.0001127711,8.735225e-7,0.0002891955,0.00006027743,0.9690867,0.0003626421,0.004256323,0.02229895],"study_design_scores_gemma":[0.0008839391,0.0009219761,0.1874721,0.00001386408,0.0002046887,0.00000706068,0.0005771302,0.0001678526,0.7296297,0.00296674,0.07666083,0.0004940646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8200998,0.0004104397,0.1759118,0.00214298,0.0000726989,0.0006732305,0.0003077656,0.00002081314,0.0003604334],"genre_scores_gemma":[0.976737,0.000214966,0.01990778,0.0008953841,0.00009527987,0.001076341,0.0001208687,0.00002747798,0.0009248788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.239457,"threshold_uncertainty_score":0.4624267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03300443535885134,"score_gpt":0.4016892001723688,"score_spread":0.3686847648135175,"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."}}