Isotopic and chromatographic fingerprinting of the sources of dissolved organic carbon in a shallow coastal aquifer
Bibliographic record
Abstract
Abstract. The terrestrial sub-surface is the largest source of freshwater globally. The organic carbon contained within it and processes controlling its concentration remain largely unknown. The global median concentration of dissolved organic carbon in groundwater is low compared to surface waters suggesting significant processing in the subsurface. Yet the processes that remove this dissolved organic carbon (DOC) in groundwater are not fully understood. The purpose of this study was to investigate the different sources and processes influencing DOC in a shallow anoxic coastal aquifer. Uniquely, this study combines liquid chromatography organic carbon detection with inorganic (δ13CDIC) and organic (δ13CDOC) carbon isotope geochemical analyses, to fingerprint the various DOC sources that influence the concentration, carbon isotopic composition and character with distance from surface water sources, depth below surface and groundwater residence time (using 3H) in groundwater. It was found that the average groundwater DOC concentration was five times higher (5 mg L−1) than the global median concentration and it doubled with depth, but the chromatographic character did not change significantly. The anoxic saturated conditions of the aquifer have limited the rate of organic matter processing leading to enhanced preservation and storage of the sources such as peats and palaeosols. All groundwater samples are more aromatic for their molecular weight in comparison to lakes and rivers and surface marine samples. The destabilisation or changes in hydrology, whether by anthropogenic or natural processes could lead to the flux of up to ten times more unreacted organic carbon from this coastal aquifer than compared to deeper inland aquifers.
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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) | 0.000 | 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".