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Record W2801537197 · doi:10.1088/1748-9326/aab63c

Nitrous oxide emissions could reduce the blue carbon value of marshes on eutrophic estuaries

2018· article· en· W2801537197 on OpenAlexafffund
Brittney L Roughan, Lisa Kellman, E. Smith, Gail L. Chmura

Bibliographic record

VenueEnvironmental Research Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsMcGill UniversityAgriculture and Agri-Food CanadaSt. Francis Xavier University
FundersSt. Francis Xavier UniversityNova Scotia Research Innovation TrustAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsSalt marshMarshEnvironmental scienceGreenhouse gasEutrophicationBlue carbonSink (geography)Carbon sinkEstuaryNitrous oxideEcosystemCarbon dioxideCarbon sequestrationEcologyWetlandNutrient

Abstract

fetched live from OpenAlex

The supply of nitrogen to ecosystems has surpassed the Earth’s Planetary Boundary and its input to the marine environment has caused estuarine waters to become eutrophic. Excessive supply of nitrogen to salt marshes has been associated with shifts in species’ distribution and production, as well as marsh degradation and loss. Our study of salt marshes in agriculturally intensive watersheds shows that coastal eutrophication can have an additional impact. We measured gas fluxes from marsh soils and verified emissions of nitrous oxide (N _2 O) in nitrogen-loaded marshes while the reference marsh was a sink for this gas. Salt marsh soils are extremely efficient carbon sinks, but emissions of N _2 O, a greenhouse gas 298 times more potent than CO _2 , reduces the value of the carbon sink, and in some marshes, may counterbalance any value of stored carbon towards mitigation of climate change. Although more research is merited on the nitrogen transformations and carbon storage in eutrophic marshes, the possibility of significant N _2 O emissions should be considered when evaluating the market value of carbon in salt marshes subject to high levels of nitrogen loading.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.913

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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

Citations35
Published2018
Admission routes2
Has abstractyes

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