{"id":"W3002527871","doi":"10.1016/j.ecoenv.2020.110229","title":"Mercury isotope compositions in large anthropogenically impacted Pearl River, South China","year":2020,"lang":"en","type":"article","venue":"Ecotoxicology and Environmental Safety","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"Guizhou Science and Technology Department; National Natural Science Foundation of China; Ministry of Science and Technology of the People's Republic of China; Agence Nationale de la Recherche","keywords":"Mercury (programming language); Environmental chemistry; Environmental science; Drainage basin; Surface water; Weathering; Biogeochemical cycle; Isotope; Hydrology (agriculture); Chemistry; Geology; Geochemistry; Environmental engineering; 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":[0.0001341352,0.0002061133,0.0002085394,0.0008508117,0.000631902,0.0003680826,0.0003041791,0.000227509,0.0005050176],"category_scores_gemma":[0.0001190233,0.0002033199,0.0001558726,0.0008926662,0.0003183256,0.0002665066,0.0003748004,0.0001219866,0.00008412221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008066411,"about_ca_system_score_gemma":0.0008468485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06117716,"about_ca_topic_score_gemma":0.0860868,"domain_scores_codex":[0.9999266,0.000004852926,0.000006040763,0.00002604839,0.00002570464,0.00001070951],"domain_scores_gemma":[0.9999139,0.000007613572,0.00001987094,0.000005060089,0.00003972189,0.00001381801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002503554,0.00005490428,0.8961004,0.00005922228,0.00009645646,0.0005825486,0.001205457,0.001007803,0.08853593,0.0002248947,0.0002198579,0.01166213],"study_design_scores_gemma":[0.000004756584,0.00002509166,0.9961666,0.000001172443,0.0000166459,0.00007522213,0.0003118358,0.0005867414,0.00243448,0.00003582948,0.0003368924,0.000004594561],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995307,0.00001901382,0.00004282747,0.000008059685,4.955467e-7,0.0000011967,0.00008986476,0.000003306893,0.0003045027],"genre_scores_gemma":[0.9991773,0.00003023436,0.00005649174,0.000007773258,0.000001040311,0.000002700293,0.0001333888,0.000002506088,0.000588551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06117716,"threshold_uncertainty_score":0.1216422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009620823798905,"score_gpt":0.2319160220736367,"score_spread":0.2222951982747317,"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."}}