Understanding the potential for disturbance-induced contaminant release from degraded peatlands: a global review of heavy metals in peatlands
Bibliographic record
Abstract
Human industry has contaminated peatlands through the atmospheric deposition of pollutants released by industrial processes over many centuries. Relative to other ecosystems, peatlands sequester a far greater proportion of toxic metals than their relatively small areal extent. This is especially true in industry-impacted landscapes, where toxic metals in surficial peat (the most likely to burn) can be elevated well above natural concentrations. Despite peatlands acting as contaminant sinks that can maintain their carbon storage functionality under low metal concentrations, high rates of metal pollution can lead to the degradation of peatland processes that sustain carbon sequestration. For example, the loss of keystone peatland species, such as Sphagnum mosses, limits peat accumulation and long-term carbon accumulation. Therefore, in these degraded peatlands, peat forming processes are often suppressed even decades after the source of contamination has reduced or ceased. Once degraded, peatlands become susceptible to additional disturbances such as fire or erosion, which can release their toxic legacy into the environment and drinking water. Predicted future warmer and drier conditions are expected to increase wildfire prevalence on the landscape and may be further compounded by land use change. The release of previously sequestered metals arguably represents one of the largest contemporary global environmental disasters and greatest future global environmental challenge. We present an in-depth review of existing literature on peatland metal contamination from a range of disciplines to provide much needed understanding of the spatial extent of peatland contamination as an essential first step in tackling contaminant release from peatland fires.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".