{"id":"W2116461220","doi":"10.1093/toxsci/kft178","title":"Incorporating New Technologies Into Toxicity Testing and Risk Assessment: Moving From 21st Century Vision to a Data-Driven Framework","year":2013,"lang":"en","type":"article","venue":"Toxicological Sciences","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","cited_by":270,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"National Institute of Environmental Health Sciences; National Institutes of Health; U.S. Environmental Protection Agency","keywords":"Metric (unit); Extrapolation; Computer science; Risk assessment; Chemical safety; In vivo; Computational biology; Biochemical engineering; Statistics; Mathematics; Biology; Engineering; Biotechnology","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":[0.0006354367,0.0002145907,0.0002578765,0.00004927572,0.0007084623,0.0004230761,0.001314476,0.0001355595,0.0004520118],"category_scores_gemma":[0.006992562,0.0001370178,0.00002408572,0.0008733169,0.0005634503,0.0006399374,0.004071753,0.0003661881,0.0001950831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009336669,"about_ca_system_score_gemma":0.00002121299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005623617,"about_ca_topic_score_gemma":0.0001970339,"domain_scores_codex":[0.9976397,0.00009828105,0.0002967086,0.001013527,0.0004564442,0.0004953939],"domain_scores_gemma":[0.9973517,0.001731266,0.000166976,0.0004143792,0.00001322656,0.0003224403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003604771,0.00009964623,0.4767178,0.000004384131,0.000005620554,0.000005262692,0.0001613517,0.001556549,0.2109492,0.0001586481,0.001273408,0.3090644],"study_design_scores_gemma":[0.0002497631,0.001619215,0.8507847,0.000248059,0.00003062572,0.000005725304,0.002279442,0.04868873,0.008971184,0.0850106,0.001237415,0.0008745943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922005,0.00005549138,0.001427514,0.003021946,0.00007688122,0.0005008428,0.00001150164,0.0002723021,0.00243305],"genre_scores_gemma":[0.6343022,0.00002646909,0.3652581,0.000340812,0.00004248132,0.00001722083,0.00000193604,0.000004423384,0.00000636291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3740668,"threshold_uncertainty_score":0.8501264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02995314682098911,"score_gpt":0.3609345698989211,"score_spread":0.330981423077932,"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."}}