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Record W4224140050 · doi:10.1029/2022jc018478

The Formation of Coastal Turbidity Maximum by Tidal Pumping in Well‐Mixed Inner Shelves

2022· article· en· W4224140050 on OpenAlexaff
Zhiyun Du, Qian Yu, Yun Peng, Li Wang, Hangjie Lin, Yunwei Wang, Shu Gao

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsMinistry of Education and Child Care
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsShoreEstuaryTurbidityOceanographySediment transportGeologySedimentTidal rangeTurbidity currentCurrent (fluid)Hydrology (agriculture)GeomorphologyGeotechnical engineeringSedimentary depositional environment

Abstract

fetched live from OpenAlex

Abstract Coastal turbidity maxima (CTMs) have been recognized on open coastal seas with elevated suspended sediment concentrations. Tidal pumping is often considered the major contributor to turbidity maxima in well‐mixed estuaries. However, its role in CTM formation on well‐mixed coasts remains unclear. Here, we propose a two‐dimensional depth‐averaged analytical model to explore residual sediment transport influenced by interactions between cross‐shore and along‐shore tidal currents. Observation data collected on the well‐mixed central Jiangsu coast (southern Yellow Sea), China, are analyzed by the model. The results display a persistent CTM close to the shore and show that the large along‐shore tidal velocity strongly influences the cross‐shore tidal pumping flux. The model calculation presents landward tidal pumping flux under horizontally homogeneous bed sediment distribution, illustrating the dominant role of tidal pumping in CTM formation within well‐mixed coastal water bodies. The model suggests that the significance of alongshore tidal current to cross‐shore sediment transport should be addressed in similar coastal environments.

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)0.003
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 score0.292
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.274
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

Citations14
Published2022
Admission routes1
Has abstractyes

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