{"id":"W2586704570","doi":"10.1038/srep42037","title":"Sediment biomarker, bacterial community characterization of high arsenic aquifers in Jianghan Plain, China","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Sediment; Environmental chemistry; Arsenic; Aquifer; Environmental science; Microbial population biology; Groundwater; Organic matter; Deposition (geology); Acidobacteria; Geology; Ecology; Chemistry; 16S ribosomal RNA; Biology; Geomorphology; Bacteria","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":[0.0002074115,0.0003537329,0.0003306041,0.001709292,0.0007969824,0.0004419191,0.0002411068,0.0003258637,0.0003191586],"category_scores_gemma":[0.0001484968,0.0002709471,0.0002026248,0.001599801,0.0002935376,0.0003337048,0.0004240385,0.0001232412,0.00006039595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000633241,"about_ca_system_score_gemma":0.001045682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04202348,"about_ca_topic_score_gemma":0.07027875,"domain_scores_codex":[0.9998305,0.00001192934,0.00001888278,0.00005148766,0.00005614662,0.00003104649],"domain_scores_gemma":[0.9998381,0.000009030918,0.00005929728,0.000004388659,0.00005459273,0.00003452422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002019902,0.0001311047,0.7818136,0.0001849965,0.00008346849,0.0006028853,0.001451952,0.0008018029,0.1991621,0.0001196481,0.0001460437,0.01530047],"study_design_scores_gemma":[0.000004534784,0.00004846713,0.9965754,0.000003237219,0.00001391973,0.0000669104,0.0003294451,0.0004463143,0.002245465,0.0000222014,0.0002382923,0.000005762871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999625,0.00005006546,0.00006803041,0.000009541402,9.457652e-7,0.000004288086,0.000102055,0.000003641751,0.0001364892],"genre_scores_gemma":[0.9988075,0.00009327442,0.0003204503,0.00001539673,0.000003832389,0.00001182796,0.0003046357,0.000001355337,0.0004416839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04202348,"threshold_uncertainty_score":0.08355772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390130167470531,"score_gpt":0.2384000196347248,"score_spread":0.2244987179600196,"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."}}