{"id":"W2782515620","doi":"10.1016/j.ecoenv.2017.12.053","title":"Tracing aquatic bioavailable Hg in three different regions of China using fish Hg isotopes","year":2018,"lang":"en","type":"article","venue":"Ecotoxicology and Environmental Safety","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; National Natural Science Foundation of China","keywords":"Methylmercury; Mercury (programming language); Environmental chemistry; Environmental science; δ13C; Aquatic ecosystem; Stable isotope ratio; Bioaccumulation; Chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000202215,0.0004149774,0.0002364666,0.001106263,0.0008748344,0.0005030809,0.0004083895,0.0003529066,0.0002766618],"category_scores_gemma":[0.0001475104,0.0002855057,0.0002919896,0.0008869471,0.0005713794,0.0004309262,0.0005295047,0.0001621198,0.00006128869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0019564,"about_ca_system_score_gemma":0.001503833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1654635,"about_ca_topic_score_gemma":0.2640229,"domain_scores_codex":[0.9998755,0.00001184781,0.000007675158,0.00004492664,0.00003458767,0.00002550038],"domain_scores_gemma":[0.9998777,0.00001327242,0.00003262809,0.000007742575,0.00005055437,0.00001811856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003917154,0.00007470651,0.8372202,0.00007496136,0.0001803176,0.0003950186,0.002689642,0.002962937,0.1420692,0.0003441207,0.0001714443,0.0134257],"study_design_scores_gemma":[0.00002080385,0.0001309494,0.9817665,0.000005557191,0.00009812056,0.00008443795,0.001278165,0.002943554,0.0128507,0.0001305091,0.0006711126,0.00001951317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994832,0.00001556977,0.0001259572,0.000007846277,7.519102e-7,0.000002849921,0.0000468379,0.000003123395,0.0003138411],"genre_scores_gemma":[0.9989685,0.00003633899,0.0002862822,0.00001019188,8.522939e-7,0.000007121036,0.0000792699,0.000002538314,0.0006089177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1654635,"threshold_uncertainty_score":0.3290009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01947951016653252,"score_gpt":0.2415627727470672,"score_spread":0.2220832625805347,"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."}}