Multi-stable isotope tracing of elevated sulfate export from a forested headwater wetland following an induced flood pulse event
Bibliographic record
Abstract
Flooding events following periods of drought can export large quantities of sulfate (SO42-) from headwater wetlands to surface waters, however the source and mechanism of SO42- release have rarely been studied. Due to the projected increases in severity and frequency of summer droughts and episodic flooding events as a result of climate change, there is a need to better understand the nature of episodic pulses of sulfate from wetlands and their downstream impacts on water quality. In this study, we monitored the evolution of the concentration and isotopic composition of surface and groundwater SO42- in Beverly Swamp, a peat marsh area in southern Ontario, Canada, during a controlled field-scale flooding event. The event was created by the rapid drawdown of the upstream located Valens Reservoir at the end of a drought period. Up to seven-fold increases in SO42- concentrations, relative to the pre-flood background levels, were observed during the flooding of the marsh. Stable S and O isotope ratios were analysed in stream and groundwaters to investigate the sources of SO42-.Following the flooding event, SO42- concentrations in the outflow from the marsh increased significantly, while δ34S-SO42- values decreased. The latter is interpreted as indicative of SO42- generated by sulphide oxidation (Schiff et al. 2005). Sulphide is likely produced by dissimilatory SO42- reduction occurring during wet conditions, with storage of the resulting sulfide minerals in the upper peat layers. During the dry summer, the sulfides are re-oxidised to SO42- and flushed from the wetland during flooding. Stable 18O-H2O isotope signatures identified water released from Valens Reservoir as the initial driver of the SO42- export across the wetland, followed by groundwater seepage from the deeper peat layers. Acidity increased shortly after the SO42- pulse, but quickly dropped down to background levels due to buffering capacity of the wetland.
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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 | 0.001 | 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".