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Record W4416121033 · doi:10.14796/jwmm.c567

The Impact of Sediment Deficiency on Riverbed Evolution in Major Mekong Delta Rivers

2025· article· W4416121033 on OpenAlexvenueno aff
Tra Nguyen Quynh Nga, Tran Thi Kim, H. Hoai, Nguyen Thi Bay

Bibliographic record

VenueJournal of Water Management Modeling · 2025
Typearticle
Language
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersViet Nam National University Ho Chi Minh CityHo Chi Minh City University of Technology and Education
KeywordsMekong deltaMekong riverDeltaErosionSedimentHydrology (agriculture)Accretion (finance)Chine

Abstract

fetched live from OpenAlex

The bed change in the Vietnamese Mekong Delta has been increasingly altered caused by natural processes and anthropogenic activities, and it becomes even more complicated under the influence of rising sea levels. At the Tan Chau and Chau Doc stations, the water volume entering the Mekong Delta did not change between 2008 and 2017, but the sediment load decreased by one-third, which caused a significant bed change in the river. This study evaluates riverbed evolution in the Mekong Delta under sediment deficiency and forecasts erosion dynamics until 2030 due to sea-level rise. Results indicate that increased riverbed erosion in 2017 is linked to a 30% drop in sediment supply compared to 2008. Simulations for 2017 indicate a 0.15% decrease in accretion rate—measured as the change in bed elevation—in the upper Tien River compared to 2008, and a 0.5% decrease in the lower reaches. Erosion rates nearly doubled in the upper reaches from Tan Chau to My Thuan, while the lower reaches showed minimal change (0.33%). By 2030, erosion will intensify, especially along the Tien River from Tan Chau to Hong Ngu, reaching 1.6 m/year. Accretion will decrease sharply, with the highest rate at 0.1 m/year near Long Khanh islet.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.231
Teacher spread0.223 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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