Dissolved Organic Matter Quality in a Shallow Aquifer of Bangladesh: Implications for Arsenic Mobility
Bibliographic record
Abstract
In some high arsenic (As) groundwater systems, correlations are observed between dissolved organic matter (DOM) and As concentrations, but in other systems, such relationships are absent. The role of labile DOM as the main driver of microbial reductive dissolution is not sufficient to explain the variation in DOM-As relationships. Other processes that may also influence As mobility include complexation of As by dissolved humic substances, and competitive sorption and electron shuttling reactions mediated by humics. To evaluate such humic DOM influences, we characterized the optical properties of filtered surface water (n = 10) and groundwater (n = 24) samples spanning an age gradient in Araihazar, Bangladesh. Further, we analyzed large volume fulvic acid (FA) isolates (n = 6) for optical properties, C and N content, and (13)C NMR spectroscopic distribution. Old groundwater (>30 years old) contained primarily sediment-derived DOM and had significantly higher (p < 0.001) dissolved As concentration than groundwater that was younger than 5 years old. Younger groundwater had DOM spectroscopic signatures similar to surface water DOM and characteristic of a sewage pollution influence. Associations between dissolved As, iron (Fe), and FA concentration and fluorescence properties of isolated FA in this field study suggest that aromatic, terrestrially derived FAs promote As-Fe-FA complexation reactions that may enhance As mobility.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".