{"id":"W2142573672","doi":"10.1021/es4051082","title":"New Insights into Traditional Health Risk Assessments of Mercury Exposure: Implications of Selenium","year":2013,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"U.S. Food and Drug Administration","keywords":"Mercury (programming language); Selenium; MERCURY EXPOSURE; Risk assessment; Exposure assessment; Environmental chemistry; Reference dose; Toxicology; Environmental health; Chemistry; Medicine; Biology; Biomonitoring; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01397606,0.001741049,0.001639398,0.00451589,0.0009889294,0.005306562,0.002838551,0.002555727,0.002378074],"category_scores_gemma":[0.0175567,0.0004221337,0.001554996,0.002188073,0.007800001,0.009073553,0.003968909,0.0035853,0.0002810932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002974235,"about_ca_system_score_gemma":0.003058779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005033025,"about_ca_topic_score_gemma":0.007626094,"domain_scores_codex":[0.9926368,0.003838033,0.000628457,0.0004606714,0.002214015,0.00022202],"domain_scores_gemma":[0.9844544,0.01143743,0.0008521549,0.0006741119,0.002301749,0.0002801786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005603329,0.0004917198,0.03279381,0.00481061,0.000641542,0.003545881,0.007351634,0.05088949,0.007770672,0.4601814,0.007137291,0.4238256],"study_design_scores_gemma":[0.0000467905,0.0006372257,0.01054225,0.001785487,0.0003304832,0.001516579,0.007270634,0.03114787,0.004080432,0.8988248,0.04367363,0.0001439774],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1674531,0.1167273,0.521022,0.1038529,0.001664075,0.0004911819,0.0009023024,0.0004405047,0.08744673],"genre_scores_gemma":[0.7560629,0.04905251,0.1850685,0.00446983,0.001294983,0.00022848,0.0002673184,0.00006922833,0.003486273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01397606,"threshold_uncertainty_score":0.0739134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162368419186692,"score_gpt":0.2708336538850677,"score_spread":0.2545968119663986,"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."}}