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Record W2592249815 · doi:10.3390/f8030075

Partitioning Forest‐Floor Respiration into Source  Based Emissions in a Boreal Forested Bog: Responses  to Experimental Drought

2017· article· en· W2592249815 on OpenAlexaffabout
Tariq Muhammad Munir, Bhupesh Khadka, Bin Xu, Maria Strack

Bibliographic record

VenueForests · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of WaterlooUniversity of Calgary
Fundersnot available
KeywordsEnvironmental scienceBogForest floorPeatTaigaRespirationSphagnumSoil respirationCarbon cycleBorealEcologyEcosystemSoil scienceSoil waterBotanyBiology

Abstract

fetched live from OpenAlex

Northern peatlands store globally significant amounts of soil carbon that could be released to the atmosphere under drier conditions induced by climate change. We measured forest floor respiration (RFF) at hummocks and hollows in a treed boreal bog in Alberta, Canada and partitioned the flux into aboveground forest floor autotrophic, belowground forest floor autotrophic, belowground tree respiration, and heterotrophic respiration using a series of clipping and trenching experiments. These fluxes were compared to those measured at sites within the same bog where water‐table (WT) was drawn down for 2 and 12 years. Experimental WT drawdown significantly increased RFF with greater increases at hummocks than hollows. Greater RFF was largely driven by increased autotrophic respiration driven by increased growth of trees and shrubs in response to drier conditions; heterotrophic respiration accounted for a declining proportion of RFF with time since drainage. Heterotrophic respiration was increased at hollows, suggesting that soil carbon may be lost from these sites in response to climate change induced drying. Overall, although WT drawdown increased RFF, the substantial contribution of autotrophic respiration to RFF suggests that peat carbon stocks are unlikely to be rapidly destabilized by drying conditions.

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 score0.074
Threshold uncertainty score0.940

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 studies0.0010.000
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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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

Citations23
Published2017
Admission routes2
Has abstractyes

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