{"id":"W2946997574","doi":"10.1016/j.cotox.2019.02.005","title":"Toxicogenomic applications in risk assessment at Health Canada","year":2019,"lang":"en","type":"article","venue":"Current Opinion in Toxicology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Agency of Canada; Wilfrid Laurier University; Health Canada","funders":"Health Canada","keywords":"Toxicogenomics; Risk assessment; Risk analysis (engineering); Health risk assessment; Data science; Computer science; Biology; Business; Computer security; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00324008,0.0009911197,0.0008453332,0.003146292,0.001206479,0.004488127,0.001777813,0.001711336,0.01171274],"category_scores_gemma":[0.007588373,0.0002597354,0.0006980245,0.003732185,0.001220112,0.001076236,0.00149088,0.002029204,0.001521466],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01160846,"about_ca_system_score_gemma":0.02280436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5488484,"about_ca_topic_score_gemma":0.5150674,"domain_scores_codex":[0.9980593,0.0005282311,0.00006033282,0.0002236157,0.000951548,0.000176943],"domain_scores_gemma":[0.9951347,0.001214906,0.0003034996,0.0001646631,0.002850031,0.0003322747],"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":[0.0004441962,0.0003198827,0.06459983,0.001439308,0.0005313945,0.001495035,0.0008716368,0.03509653,0.01715982,0.06584413,0.08558832,0.7266098],"study_design_scores_gemma":[0.0001036651,0.0003349866,0.1007637,0.001677029,0.0006832584,0.001195677,0.003725865,0.08765771,0.03249617,0.2098418,0.5612005,0.0003197863],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.08677796,0.1010371,0.3896386,0.1678958,0.003602185,0.001069878,0.02516638,0.003908326,0.2209038],"genre_scores_gemma":[0.6754081,0.1040946,0.1614918,0.01580585,0.001544921,0.0002735582,0.004577419,0.0004888791,0.03631498],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9883915,"threshold_uncertainty_score":0.9076171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712355598420261,"score_gpt":0.3276985836524229,"score_spread":0.3105750276682203,"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."}}