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Record W4323357530 · doi:10.1002/fee.2611

Invasive <i>Spartina alterniflora</i> marshes in China: a blue carbon sink at the expense of other ecosystem services

2023· review· en· W4323357530 on OpenAlexaff
Xiangzhen Qi, Gail L. Chmura

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsMcGill University
FundersPriority Academic Program Development of Jiangsu Higher Education Institutions
KeywordsSpartina alternifloraBlue carbonMarshWetlandEnvironmental scienceSalt marshCarbon sinkSoil carbonGreenhouse gasEcosystemSink (geography)Carbon sequestrationEcologyDominance (genetics)Ecosystem servicesSpartinaSoil waterGeographyCarbon dioxideBiology

Abstract

fetched live from OpenAlex

Coastal (marine) wetlands are recognized as one of the world's most efficient sinks for organic carbon (“blue carbon”), and credits for their restoration and conservation can be obtained from carbon markets. We reviewed 50 studies to compare the climate mitigation potential of the invasive cordgrass Spartina alterniflora to that of native vegetation in marshes along the coast of China. The importance of S alterniflora marshes as a carbon sink varied geographically; however, at all sites soils associated with S alterniflora emitted substantially more methane than soils populated with native plants. Because the species was deliberately introduced, the carbon stored in S alterniflora marshes could qualify for inclusion within China's national inventory of greenhouse‐gas emissions. However, in locations where it significantly increases carbon stocks, its dominance results in the loss of other ecosystem services. Therefore, if included in a national inventory , S alterniflora marshes could conflict with China's action plan for meeting UN Sustainable Development Goals.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.008
GPT teacher head0.205
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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations41
Published2023
Admission routes1
Has abstractyes

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