{"id":"W1972063733","doi":"10.1002/etc.2113","title":"Mercury in the seafood and human exposure in coastal area of Guangdong province, South China","year":2012,"lang":"en","type":"article","venue":"Environmental Toxicology and Chemistry","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Genomics; University of Ottawa","funders":"Institute of Geochemistry, Chinese Academy of Sciences; Guangzhou Institute of Geochemistry, Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Shrimp; Methylmercury; Mercury (programming language); Tolerable daily intake; China; Fishery; Population; Human health; Toxicology; Fish consumption; Geography; Environmental protection; Fish <Actinopterygii>; Biology; Environmental health; Ecology; Bioaccumulation; Body weight; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000280246,0.0002638263,0.0001284987,0.0006753207,0.0006088087,0.0002656603,0.0001942282,0.0001922394,0.0009258359],"category_scores_gemma":[0.0003601201,0.0001976805,0.0002163233,0.001178411,0.0004207453,0.000206318,0.0003737782,0.0001794866,0.00009177125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008200157,"about_ca_system_score_gemma":0.000923944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1002391,"about_ca_topic_score_gemma":0.1270842,"domain_scores_codex":[0.9998804,0.0000202088,0.0000135528,0.0000372532,0.00002940369,0.00001900841],"domain_scores_gemma":[0.9998068,0.00001944731,0.00007688567,0.00001491686,0.00003395001,0.00004802947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004280786,0.00001662798,0.9961759,0.00001747948,0.00002905352,0.0001444096,0.0005152801,0.00006321447,0.001060086,0.00002296482,0.00007946615,0.001832688],"study_design_scores_gemma":[0.000002612563,0.00002027806,0.9994889,0.000001957324,0.000006089872,0.00004218558,0.0002207325,0.00003817068,0.00005296746,0.00001348862,0.0001113258,0.000001171044],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995341,0.00008310589,0.00001581717,0.00004811006,0.000001298038,0.000002812994,0.000108955,0.000001184247,0.0002045392],"genre_scores_gemma":[0.9993821,0.000151352,0.00004790087,0.00002076999,0.000002549513,0.000004782265,0.0001352384,6.391013e-7,0.0002547646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1002391,"threshold_uncertainty_score":0.1993113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01049420874945066,"score_gpt":0.2247809956544783,"score_spread":0.2142867869050277,"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."}}