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Record W7161944918 · doi:10.82308/25831

Quantifying soil carbon storage and losses in natural and agriculturally converted salt marsh

2016· dissertation· en· W7161944918 on OpenAlexaboutno aff
Lee Van Ardenne

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSalt marshSoil carbonBlue carbonMarshTemperate climateCarbon sequestrationCarbon fibersEcosystemGreenhouse gasAtmospheric carbon cycle

Abstract

fetched live from OpenAlex

Coastal vegetated ecosystems, such as salt marshes, actively sequester large amounts of carbon from the atmosphere and can store this carbon in their soils for millennia. These ecosystems have been badly degraded from anthropogenic activity over time, with evidence suggesting that their stores of carbon can be released to the atmosphere as a result. There is a general lack of studies which report on the carbon storage in salt marshes over the full depth of the soil deposit, and it is not well established just how much carbon is stored in these systems. Correspondingly, 1 m is often used as a default in estimates. Thus it is largely unknown what geomorphic or environmental parameters drive differences in carbon storage between marshes. How much carbon is lost when drained for agriculture or other land use change has a similar paucity in studies, and those which do exist are geographically biased to warm temperate climates. This thesis reports on two projects which seek to address these gaps in research.The chapter two study sought to estimate the total carbon stock of four salt marshes along the coasts of New Brunswick, Canada, and Maine, USA using GIS interpolation and identify any spatial trends or relations to general climate and geomorphic conditions. The spatial distribution of soil in the marshes was similar to developmental models of developed for similar marsh types in literature, with soil depth the greatest in the center of the marsh and declining towards the upland and seaward margins. The average carbon storage and carbon densities of the marshes were lower than current global averages. Average carbon density with depth was very stable except a single notable decrease which occurred at a breakpoint at 50 cm depth – likely due to carbon losses from the rapid decay of labile carbon in the rooting zone. Thus a very strong linear relationship between soil depth and carbon storage was found, which would allow for estimations of carbon storage using just soil depth in marshes of similar characteristics. This also indicates that assuming a soil depth (such as 1 m) is not an acceptable method when estimating carbon stocks. Comparing interpolation results to simple averages of the cores indicated that a single transect of cores could acceptably estimate soil depth (thus carbon storage) in marshes with simple morphology, and that one transect per axis of soil depth variation may work in more complex marsh systems.The chapter three study measured carbon stocks and calculated losses for a series of drained marshes and paired undrained marsh along the Kamouraska region of the St. Lawrence River, Quebec, Canada. The estimated rate of loss averaged 459 g C m-2 yr-1, with overall losses varying between 15% and 39% of the original amount since drainage. The rate is lower than the IPCC default emission factor and most current rates reported in literature, which are from warm temperate climates, and suggests that rates of carbon loss may vary in different climates. Using the average rate of loss for the St. Lawrence region, the total estimated carbon loss in the region since 1987 (further dyking was banned after this date) is 1,673,055 t C.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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
Published2016
Admission routes1
Has abstractyes

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