The effects of fire on sulfidic peat swamp sediments
Bibliographic record
Abstract
Peat swamps contain substantial accumulations of organic matter due to waterlogging and slower decomposition rates. Peat swamps can be underlain by sulfidic sediments where there is abundant iron and sulfate for reduction to form a range of sulfidic minerals, primarily pyrite (FeS2). Sulfidic sediments can acidify to produce sulfuric acid, similar to acid mine drainage (AMD) and acid sulfate soil (ASS) environments when oxidised, which can occur when water levels drop due to drainage or periods of drought. Discharging surface and shallow groundwater can therefore acidify adjacent lakes and waterways. These swamps can also present significant fire hazards when drying occurs. This study identified the chemical and mineralogical changes in sulfidic peat swamp sediments along a temperature gradient to simulate the effects of fire. We found that fire induced changes in the Fe-minerals to form a range iron (oxy)hydroxides and iron oxides such as magnetite, mghemite and haematite in increasing crystallinity with increasing temperatures. pH initially decreased on drying a minimum of pH 3.15, before increasing with increasing temperature to 650oC to pH 4.86, which can mobilise environmentally important pH-sensitive metals. Peat swamps are highly susceptible to the effects of fire when surface- and shallow groundwater levels decrease as a result of extended drought or drainage. Fire can irreversibly alter underlying soil properties to induce changes in soil minerals and potentially impact the surrounding environment.
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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".