Stimulation of Microbially Mediated Arsenic Release in Bangladesh Aquifers by Young Carbon Indicated by Radiocarbon Analysis of Sedimentary Bacterial Lipids
Bibliographic record
Abstract
The sources of reduced carbon driving the microbially mediated release of arsenic to shallow groundwater in Bangladesh remain poorly understood. Using radiocarbon analysis of phospholipid fatty acids (PLFAs) and potential carbon pools, the abundance and carbon sources of the active, sediment-associated, in situ bacterial communities inhabiting shallow aquifers (<30 m) at two sites in Araihazar, Bangladesh, were investigated. At both sites, sedimentary organic carbon (SOC) Δ(14)C signatures of -631 ± 54‰ (n = 12) were significantly depleted relative to dissolved inorganic carbon (DIC) of +24 ± 30‰ and dissolved organic carbon (DOC) of -230 ± 100‰. Sediment-associated PLFA Δ(14)C signatures (n = 10) at Site F (-167‰ to +20‰) and Site B (-163‰ to +21‰) were highly consistent and indicated utilization of carbon sources younger than the SOC, likely from the DOC pool. Sediment-associated PLFA Δ(14)C signatures were consistent with previously determined Δ(14)C signatures of microbial DNA sampled from groundwater at Site F indicating that the carbon source for these two components of the subsurface microbial community is consistent and is temporally stable over the two years between studies. These results demonstrate that the utilization of relatively young carbon sources by the subsurface microbial community occurs at sites with varying hydrology. Further they indicate that these young carbon sources drive the metabolism of the more abundant sediment-associated microbial communities that are presumably more capable of Fe reduction and associated release of As. This implies that an introduction of younger carbon to as of yet unaffected sediments (such as those comprising the deeper Pleistocene aquifer) could stimulate microbial communities and result in arsenic release.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".