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Record W4248638722 · doi:10.13031/2013.20291

CLIMATE - CARBON DYNAMICS IN NORTHERN PEATLANDS

2013· article· en· W4248638722 on OpenAlexaboutno aff
N.T. Roulet, S. Frolking, P. Lafleur, T. Moore, and P. Richard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsPeatEnvironmental scienceCarbon sinkGreenhouse gasCarbon cycleSink (geography)WetlandClimate changeBiosphereCarbon fibersAtmospheric sciencesMireDissolved organic carbonCarbon dioxideEcosystemEcologyGeologyGeography

Abstract

fetched live from OpenAlex

Northern wetlands contain 30% of the worlds terrestrial carbon store and are believed to be a contemporary sink of CO2 (0.1 to 0.5 Pg C yr-1) and a source of CH4 (10 C 30 Tg C yr-1). Until recently, most of the studies on the atmosphere-biosphere exchange of greenhouse gases from northern peatlands were short-term and seasonal, and there were few models of peatland carbon cycling that could be used in climate simulations. In 1998 the Peatland Carbon Study began continuous measurements of the carbon dynamics of a northern peatland. This research has continued, now as part of the Fluxnet Canada Research Network. As part of the Land Surface Processes node of the former Climate Research Network and now as an integral part of the Global Canadian Coupled Carbon Climate Model research network, we have developed several ecosystem models to simulation the response of northern peatlands to climate variability and change. The continuous measurements indicate that over a six-year, the C balance can be either positive (source to the atmosphere) or negative (net sink). Further our results show that one or two years worth on measurements are inadequate to conclude the role of a peatland as a sink or source, and that a C balance without the inclusions of the losses of C via the export of dissolved organic carbon (DOC) and/or CH4, is too uncertainty to be useful in climate-greenhouse gas exchange assessments. Development, evaluation and sensitivity analysis of two different process models, the Peatland Carbon Simulator (PCARS), and a more recent McGill Wetland Model (McWM), show the critical importance of climate, hydrology and plant community structure to simulating peatland greenhouse gas exchanges. If the hydrology of a peatland can not be simulated with reasonable success C e.g. water table evaluation with 0.1 m accuracy, then the results of the ecosystems models are too large to help in climate and greenhouse gas assessments. This presents a huge challenge for the peatland modelling community. We have been reasonably successful in simulated peatland hydrology and surface climate for northern bogs and mineral poor fens using a wetland version of the Canadian Land Surface Scheme at a point, but scaling the models to the region and global remains elusive. Using a long-term paleo-ecological model for northern peatlands, the Peat Accumulation Model (PAM), and a very simple carbon cycle and radiative forcing model, we have examined how the sink/source function of northern peatlands involving multiple greenhouse gases C e.g. CO2 and CH4, can be assessed. We have concluded, in all but constant conditions, the Global Warming Potential, that is widely used by the peatland community, is not a particularly, appropriate metric.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes1
Has abstractyes

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