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Record W4410827589 · doi:10.3897/aca.8.e155498

What a peaty contribution to global warming! An interdisciplinary study of atmospheric and hydrologic carbon fluxes in a temperate peatland in the Jura Mountains, eastern France

2025· article· en· W4410827589 on OpenAlexaff
Noémie Poteaux, Alexandre Lhosmot, Marc Steinmann, Adrien Jacotot, Philippe Binet, Sarah Coffinet, Eliot Chatton, Camille Bouchez, Robin Calisti, Edward A. D. Mitchell, Daniel Gilbert, Anne Boetsch, Marie‐Laure Toussaint, Lilian Joly, Laurent Longuevergne, Vincent Milesi, Marie‐Noëlle Pons, Nicolas Dumelié, Christophe Loup, Jean‐Louis Bonne, Delphine Combaz, Virginie Girard Girard, Guillaume Bertrand

Bibliographic record

VenueARPHA Conference Abstracts · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPeatTemperate climateEnvironmental sciencePhysical geographyClimatologyGlobal warmingClimate changeGeographyGeologyOceanographyEcologyArchaeology

Abstract

fetched live from OpenAlex

Peatlands, though covering only 3 % of the global land surface, play an active role in the Critical Zone (CZ) by mediating substantial water and carbon exchanges with adjacent aquifers, surface waters, and the atmosphere. These ecosystems provide key services, such as carbon and water storage and local climate regulation, addressing contemporary challenges related to climate change, biodiversity loss, and water resource management. However, peatlands are increasingly threatened by global pressures, including climate change, and local disturbances, such as drainage for agriculture, forestry, and peat extraction. To mitigate these threats, it is essential to understand the hydrological, biogeochemical, and ecological processes governing peatland dynamics across spatiotemporal scales. To explore the factors controlling greenhouse gases sources, production, and transport in peatlands, an interdisciplinary field campaign was conducted at the Frasne peatland (7 ha, 46.826°N, 6.1754°E, 840 m a.s.l.), a long-term observatory since 2008. The site is part of the French CZ research infrastructure (OZCAR) and the long term ecological research site Jurassian Arc, which focuses on the interaction between human and nature. The campaign was supported by the TERRA FORMA project, which develops smart, connected, low-cost, and low-impact environmental sensors to monitor CZ trajectories in the Anthropocene. The fieldwork integrated microbiological analyses of peat material, including membrane lipid profiling to trace microbial metabolisms, combined with detailed hydrogeochemical investigations of peat pore water along lateral flow and depth gradients. Measurements included physicochemical parameters (temperature, electrical conductivity, pH) and major elements, dissolved organic and inorganic carbon (DOC and DIC), CO₂, and CH₄ concentration, as well as their isotopic characterization (δ¹⁸O, δ²H, δ¹³C) . Additionally, greenhouse gases fluxes were quantified at multiple scales, employing methods such as dissolved gas profiling, chamber measurements, eddy covariance, and UAV-based surveys. This multiscale approach aims to tackle critical challenges in peatland research and management, including three-dimensional quantification of carbon fluxes (lateral and vertical) at the ecosystem scale; characterization of hydrological, biogeochemical, and ecological processes that modulate greenhouse gases and dissolved carbon production and transport; and development of accessible and efficient tools for addressing these pressing environmental issues. three-dimensional quantification of carbon fluxes (lateral and vertical) at the ecosystem scale; characterization of hydrological, biogeochemical, and ecological processes that modulate greenhouse gases and dissolved carbon production and transport; and development of accessible and efficient tools for addressing these pressing environmental issues.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.282
Teacher spread0.273 · 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 teacher head, 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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