{"id":"W2792138730","doi":"10.1016/j.scitotenv.2018.01.322","title":"Assessment of Cr pollution in tributary sediment cores in the Three Gorges Reservoir combining geochemical baseline and in situ DGT","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"State Key Laboratory of Automotive Simulation and Control; China Institute of Water Resources and Hydropower Research","keywords":"Tributary; Environmental science; Sediment; Environmental chemistry; Pollution; Surface water; Hydrology (agriculture); Geology; Environmental engineering; Chemistry; Geomorphology; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006652675,0.000194868,0.0002596192,0.00007824476,0.0001723539,0.00001531875,0.001154531,0.00005910854,0.0002268009],"category_scores_gemma":[0.0001514052,0.0001081119,0.0000551927,0.000573835,0.004910799,0.0002094025,0.001476722,0.0003220865,0.00001391015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006337035,"about_ca_system_score_gemma":0.00003406654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006709581,"about_ca_topic_score_gemma":0.000200145,"domain_scores_codex":[0.9967107,0.0004034234,0.0005904904,0.0004642133,0.00135289,0.0004783538],"domain_scores_gemma":[0.9986414,0.0002222223,0.0002127919,0.0008573044,0.000002744959,0.0000635121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008358431,0.0008802093,0.07015354,0.00001395303,0.000008033101,0.00000442085,0.002343983,0.1269323,0.7977839,0.0002461852,0.00007756685,0.001472344],"study_design_scores_gemma":[0.0005297184,0.0001714364,0.9170905,0.00005468336,0.0000113176,0.00000832247,0.0005815557,0.01729373,0.06282479,0.001264186,0.00004385828,0.0001258657],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942759,0.0000791427,0.00005239319,0.003844942,0.00008777546,0.000664385,0.00000845046,0.000003391029,0.0009835977],"genre_scores_gemma":[0.998955,0.00006325084,0.0007721937,0.0001115768,0.00001913675,0.0000383823,0.000001266866,0.000008257295,0.00003095681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.846937,"threshold_uncertainty_score":0.9977973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595145723346317,"score_gpt":0.2621187829583562,"score_spread":0.246167325724893,"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."}}