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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".