{"id":"W2792033749","doi":"10.1016/j.scitotenv.2018.02.224","title":"Bioaccessibility-corrected risk assessment of urban dietary methylmercury exposure via fish and rice consumption in China","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"Chinese Academy of Medical Sciences; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Academy of Medical Sciences; Chinese Academy of Sciences; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Methylmercury; Environmental science; Hazard quotient; Fish <Actinopterygii>; Fish consumption; Reference dose; Contamination; Environmental chemistry; Toxicology; China; Risk assessment; Chemistry; Bioaccumulation; Biology; Fishery; Ecology; Geography","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":[0.001189432,0.0006142664,0.0003647813,0.001108189,0.0003890538,0.0004647731,0.0005222903,0.0003693543,0.0005056315],"category_scores_gemma":[0.000504323,0.0003616697,0.001023403,0.0009599957,0.0003388405,0.0003053987,0.0006123985,0.0002035374,0.00009159893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001805089,"about_ca_system_score_gemma":0.001794897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1092832,"about_ca_topic_score_gemma":0.09217032,"domain_scores_codex":[0.9996632,0.00007227022,0.00002775477,0.00009317591,0.00008565687,0.00005784243],"domain_scores_gemma":[0.9996289,0.00004576363,0.0001272523,0.00004745802,0.0001134588,0.00003715841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00111354,0.0001007725,0.975536,0.0000543641,0.0005695308,0.0002848736,0.0004551855,0.006118937,0.007965643,0.0002607984,0.000121542,0.007418871],"study_design_scores_gemma":[0.00002551178,0.0003000381,0.9877005,0.000005888697,0.0004326706,0.00009705695,0.0003304791,0.007908218,0.002530538,0.0002105908,0.0004399574,0.00001861254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993156,0.00006948962,0.0002842035,0.00001587306,0.000001122173,0.000006028597,0.0001423515,0.000004009762,0.0001613036],"genre_scores_gemma":[0.9991826,0.00006139029,0.0001512052,0.000008155117,0.000001230837,0.000006228177,0.0001763691,0.000002030716,0.0004107993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1092832,"threshold_uncertainty_score":0.2172941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01447287929527947,"score_gpt":0.2672034793641739,"score_spread":0.2527306000688945,"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."}}