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Record W2140915423 · doi:10.1177/0959683614538078

Exploring the relationship between peatland net carbon balance and apparent carbon accumulation rate at century to millennial time scales

2014· article· en· W2140915423 on OpenAlexaff
Steve Frolking, Julie Talbot, Zack M Subin

Bibliographic record

VenueThe Holocene · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité de Montréal
FundersNational Centre for Biological SciencesLehigh UniversityNational Science Foundation
KeywordsPeatCarbon fibersEnvironmental scienceCarbon cycleAtmospheric sciencesClimate changeClimatologyPhysical geographyPaleoclimatologySoil scienceGeologyEcosystemEcologyGeographyMathematicsOceanography

Abstract

fetched live from OpenAlex

Each year, a peatland has an annual net carbon balance ( NCB), which can be positive (net uptake), zero or negative. Over centuries to millennia, this NCB accumulates as a peat profile. Contemporary peatlands can be sampled (cored), and the past apparent carbon accumulation rate ( aCAR) can be determined as the quantity of peat carbon in any particular dated interval down the core profile. We use a process-based peatland carbon and water cycle model to compare peatland annual NCB during millennia of peat accumulation to the contemporary estimate of aCAR, resulting from this accumulation. Integrating over the entire profile, the accumulated NCB must equal the aCAR, but for shorter time intervals, these two quantities can diverge. A climate variation/perturbation that leads to persistent, slow carbon loss or negligible carbon gain through enhanced decomposition will necessarily reduce the aCAR for time periods before the climate variation/perturbation occurred. This can compromise peatland climate–carbon balance relationships inferred from joint analysis of peat cores and paleoclimate reconstructions.

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)0.001
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 score0.004
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.056
GPT teacher head0.253
Teacher spread0.197 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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