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Record W1847525563 · doi:10.1139/cjb-2015-0048

The influence of climate on pool inception in boreal fens

2015· article· en· W1847525563 on OpenAlexafffundvenueabout
Yann Arlen-Pouliot, Serge Payette

Bibliographic record

VenueBotany · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsCenter for Northern Studies
FundersHydro-QuébecNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBorealPeatBiomeEcologyAlternative stable stateTaigaClimate changeDisturbance (geology)Environmental scienceDendroclimatologyPhysical geographyEcosystemBiologyGeography

Abstract

fetched live from OpenAlex

Fens are one of the two most important peatland types of the boreal biome. The fen surface is often made of contrasted microenvironments, pools and strings, distributed in a geometric arrangement known as patterned fen. The fens are under the influence of varying water regimes causing the formation of pools, a process that we named aqualysis. The term refers to the physical degradation of the vegetation cover under the influence of water ponding. It is proposed here that pool inception is among a set of differential responses of peatland ecosystems to changes in hydrology caused by climate. In this study, we have evaluated the influence of climate on pool inception using the spatiotemporal distribution of trees found dead in pools of four boreal fens of northwestern Quebec. Tree-ring dating of tree mortality allowed the determination of the most recent and synchronized periods of pool formation in the studied fens. Most trees died over the last centuries, particularly after 1750 AD. The demographic pattern of tree establishment and mortality highlights a climatic forcing linked to the Little Ice Age oscillation opposing less humid events facilitating tree colonization succeeded by more humid events causing massive tree death and pool inception. We conclude that peatland aqualysis is among the processes controlled by climate contributing to the dynamics of patterned fens through pool formation.

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 score0.019
Threshold uncertainty score0.157

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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

Citations10
Published2015
Admission routes4
Has abstractyes

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