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Record W4410714053 · doi:10.1029/2025gl115606

Coastal Erosion as a Major Sediment Source in the Inner Gulf of Thailand: Implications for Carbon Dynamics in Tropical Coastal Ocean Systems

2025· article· en· W4410714053 on OpenAlexaff
Bingbing Wei, Stephanie Kusch, Till J J Hanebuth, Limin Hu, Miao Fan, Guodong Jia, Gesine Mollenhauer, Moritz Holtappels

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsCégep de RimouskiUniversité du Québec à Rimouski
FundersAlfred Wegener Institute Helmholtz Centre for Polar and Marine ResearchDeutsche Forschungsgemeinschaft
KeywordsOceanographySedimentErosionCoastal erosionGeologyTropical cycloneEnvironmental scienceTropical marine climateClimatologyHydrology (agriculture)GeomorphologyMeteorologyGeographyShore

Abstract

fetched live from OpenAlex

Abstract Coastal erosion is an increasingly dominant sediment source in marginal seas, particularly in low‐lying areas affected by deltaic subsidence and sediment deficits from upstream water management. However, its role in sediment and organic carbon (OC) dynamics remains to be estimated. Our analyses of the inner Gulf of Thailand (IGoT) revealed that riverine sediment fluxes decreased from 6.6 to 5.4 Mt/yr after 1975, while sediment accumulation within the IGoT increased from 20.8 to 29.5 Mt/yr. The observed trend indicates major sediment contributions from coastal erosion, particularly from mangrove deposits. This process destabilizes coastal ecosystems and accelerates OC decomposition, that is, a low burial efficiency (16.8 ± 5.5%) leads to CO 2 release. Extrapolating these findings globally, mangrove loss could release ∼175 Tg/yr CO 2 . As coastal erosion intensifies under sea‐level rise and human land‐use practices, preserving coastal ecosystems is critical for mitigating blue carbon loss and maintaining coastal stability and resilience.

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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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