Peat fires and the unknown risk of legacy metal and metalloid pollution
Bibliographic record
Abstract
Introduction. Peatlands have persisted for millennia, acting as\nglobally-important sinks of atmospheric carbon\ndioxide (Yu 2012) and regionally-important role\nsinks of pollutants, such as lead, arsenic, or mercury (toxic metals and metalloids, TMMs) (Bindler\n2006). The role peatlands play in atmospheric carbon sequestration often overshadows their role in\nstoring pollutants despite, for example, peat mercury\naccumulation rates increasing 60–130× relative to\npre-industrial rates (Bindler 2006). Peatlands sustain\ntheir carbon and TMM sink persistence through a\nsuite of ecohydrological feedbacks and plant traits\n(Souter and Watmough 2016, McCarter et al 2020).\nHowever, the interaction of climate change, land-use\nchange and wildfire are testing peatland resilience\n(Wilkinson et al 2023), potentially placing their longterm stores of recent and legacy carbon and TMMs\non the edge of catastrophic release.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".