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Record W4408446270 · doi:10.5194/egusphere-egu25-296

Global stocks and release pathways of pollutants in peatlands

2025· preprint· en· W4408446270 on OpenAlexaff
Richard E. Fewster, Graeme T. Swindles, Gareth D. Clay, Emma Shuttleworth, Jennifer M. Galloway, Angela Gallego‐Sala, Thomas J. Kelly, Colin P. R. McCarter, Ellie Purdy, Jim Sloan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsNipissing UniversityGeological Survey of CanadaCarleton University
Fundersnot available
KeywordsPeatPollutantEnvironmental scienceChemistryEcologyBiology

Abstract

fetched live from OpenAlex

Peatlands have been widely recognised as important carbon stores, ecological habitats and natural hydrological buffers. However, comparatively less attention has been given to the role of peatlands as long-term stores of pollutants, particularly toxic metals and metalloids (TMMs). Furthermore, the potential for their release is poorly understood. An improved understanding of TMM distribution and release in peatlands is critical, because climate warming risks increasing their mobilisation, through enhanced decomposition and changes to hydrological processes, with potentially significant implications for natural ecosystems and human health. The PIPES project (Pollutants In Peatlands: from sink to Source) aims to identify global “hot spots” of peatland pollutants and establish likely release mechanisms of currently inert TMMs. We use a unique combination of observational and controlled-experimental approaches to address two research questions: (1) What is the content and distribution of pollutants in global peatlands? and (2) Under what conditions, and through which pathways, are these pollutants most likely to be released? In this presentation, we share early findings from both components of the PIPES project. Firstly, we present our ongoing analysis of the distribution of TMMs in global peatlands, with a primarily focus on spatial patterns identified across our comprehensive network of sites in the UK and Ireland. We quantify the total content of TMMs using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) in peat cores compiled by a network of > 90 international collaborators. Secondly, we present preliminary results from controlled environmental simulations of TMM release in peat monoliths from subarctic Sweden. We explore both pore-water and atmospheric release under scenarios of drought, climate warming and a shallow burn. Our findings provide crucial new insights into the potential fate of pollutants in global peatlands and their implications for human health and natural ecosystems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.239
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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