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Record W4406309837 · doi:10.1016/j.trac.2025.118153

Natural organic matter dynamics in permafrost peatlands: Critical overview of recent findings and characterization tools

2025· article· en· W4406309837 on OpenAlexaff
Diogo Folhas, Raoul‐Marie Couture, Isabelle Laurion, Gonçalo Vieira, João Canário

Bibliographic record

VenueTrAC Trends in Analytical Chemistry · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité LavalCenter for Northern Studies
FundersInstituto Superior TécnicoFundação para a Ciência e a Tecnologia
KeywordsPermafrostPeatCharacterization (materials science)Natural (archaeology)Environmental scienceEarth sciencePhysical geographyGeologyEcologyGeographyOceanographyPaleontologyNanotechnologyBiologyMaterials science

Abstract

fetched live from OpenAlex

Rising temperatures are destabilizing permafrost in northern latitudes, leading to the mobilization, transformation and cycling of natural organic matter (NOM), nutrients, and contaminants into newly formed aquatic systems. Analyzing the chemical composition of organic matter is crucial for understanding the biogeochemical processes at play. Furthermore, it is essential to investigate how seasonal variations and anoxic conditions influence these processes, as well as their effects on microbial activity and NOM composition. This review provides an overview of northern peatlands , terminal electron acceptor species, and key analytical techniques used to characterize organic matter: UV/Vis and Fluorescence Spectroscopy , FTIR , FT-ICR-MS, and Nuclear Magnetic Resonance. Rather than focusing on the theoretical aspects of these techniques, we emphasize the type of information they offer about NOM and how to interpret these data within the context of biogeochemical transformations in permafrost-affected systems.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.994

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)0.0070.000

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 head0.016
GPT teacher head0.287
Teacher spread0.271 · 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.

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

Citations7
Published2025
Admission routes1
Has abstractyes

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