{"id":"W3203933639","doi":"10.1016/j.comtox.2021.100191","title":"In silico approaches in carcinogenicity hazard assessment: Current status and future needs","year":2021,"lang":"en","type":"article","venue":"Computational Toxicology","topic":"Carcinogens and Genotoxicity Assessment","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada; Health Canada","funders":"National Institute of Environmental Health Sciences; National Institutes of Health","keywords":"In silico; Scope (computer science); Protocol (science); Computer science; Hazard; Risk analysis (engineering); Computational biology; Biology; Business; Medicine","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":[0.0001662953,0.000151773,0.0002125075,0.0001364316,0.00004761812,0.00002437298,0.00006475579,0.0001547381,0.00004257943],"category_scores_gemma":[0.00001854265,0.0001691433,0.00005588057,0.0002564254,0.00007647462,0.000006634433,0.0001890892,0.0002002601,0.000001590282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009082922,"about_ca_system_score_gemma":0.0004693973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000120668,"about_ca_topic_score_gemma":0.0004936396,"domain_scores_codex":[0.9987397,0.0001714221,0.0002612401,0.0003912058,0.0001297932,0.000306686],"domain_scores_gemma":[0.999609,0.0000347974,0.00005432519,0.0001478455,0.00006544863,0.00008857426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002334279,0.001650213,0.8062757,0.0001670444,0.0001268809,0.000159031,0.0007895662,0.03368615,0.1021351,0.02477403,0.0009598724,0.029043],"study_design_scores_gemma":[0.001782534,0.0002297261,0.9585953,0.00000797097,0.00001255624,0.00008432424,0.000588734,0.005090979,0.006297009,0.002324992,0.02469143,0.0002944026],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928163,0.004088577,0.0009929911,0.001184175,0.0002944066,0.000181182,0.00004146712,0.000004612678,0.0003962535],"genre_scores_gemma":[0.9965291,0.0004309121,0.001803545,0.0004674671,0.0002813076,0.00004200596,0.0003874452,0.00001185855,0.00004637044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1523196,"threshold_uncertainty_score":0.6897461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769727882072802,"score_gpt":0.3026332889388217,"score_spread":0.2749360101180937,"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."}}