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Record W4417451854 · doi:10.1002/eco.70158

Hydrological Feedbacks in Northern Peatlands 2: Peat Depth as a Control on Peatland Resilience

2025· article· en· W4417451854 on OpenAlexafffund
Alex Furukawa, Owen F. Sutton, Kyra Simone, Gregory J. Verkaik, Paul Moore, Alexandra Clark, Rachel Y. Fallas, M. J. Moore, Emma Sherwood, Rosanne C. Broyd, Brandon Van Huizen, Paul J. Morris, J. M. Waddington

Bibliographic record

VenueEcohydrology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of WaterlooMcMaster University
FundersGlobal Water FuturesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsPeatEcohydrologyHydrology (agriculture)Climate changeVegetation (pathology)Ecosystem

Abstract

fetched live from OpenAlex

ABSTRACT As climate change increasingly threatens the northern peatland net carbon sequestration function, there is a pressing need to better understand the limits of ecohydrological regulatory mechanisms. This is especially urgent for shallow peatlands (< 40‐cm average peat depth), which consistently experience water stress with greater intensity, frequency and duration than deep peatlands and may represent sentinels for climate change. In this ‘part 2’ paper, we review the peatland hydrological feedbacks originally proposed a decade prior in Hydrological Feedbacks in Northern Peatlands ‘part 1’ (Waddington et al. 2015) to investigate the strength of feedback mechanisms as a function of peat depth. We show that in some hydrogeomorphic and hydroclimatic settings there are differences in hydrophysical properties and vegetation cover between shallow and deep peatlands. These structural characteristics influence the strength of the fast (i.e., function on a timescale of seconds to days) hydrological feedbacks (moss surface resistance and albedo, transmissivity, peat deformation and specific yield). In contrast, the slow feedbacks (i.e., operating on the scale of months to decades) related to vegetation community change and peat decomposition directly impact peatland physical characteristics (patterns and composition of vegetation, bulk density, etc.). We discuss how the vulnerability of shallow peatlands arises from the interactions between regulatory (negative) and destabilizing (positive) ecohydrological feedbacks.

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 score0.202
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.230
Teacher spread0.226 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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