Process Study of Dry-Season Circulation in the Pearl River Estuary and Adjacent Coastal Waters using a Triple-Nested Coastal Circulation Model
Bibliographic record
Abstract
The Pearl River Estuary (PRE) on the east coast of Guangdong Province in South China is a complicated hydrodynamic system affected by various forcing functions including tides, wind forcing, and sea surface heat and freshwater fluxes. The PRE also receives a large amount of freshwater runoff from the Pearl River through eight major river inlets. In this study, the three-dimensional circulation, hydrography, and associated temporal variability in the PRE and adjacent coastal waters during the dry season from December to March are examined using a triple-nested coastal ocean circulation modelling system based on the Princeton Ocean Model. Four numerical experiments are conducted by driving the triple-nested modelling system with different combinations of external forcing functions. Analysis of multi-year model results from the four experiments demonstrates that the estuarine plume in the dry season is close to the western shore of the PRE, mainly due to the combination of the low Pearl River discharge and the influence of the southwestward coastal current over the inner shelf of the northern South China Sea. Temperature and salinity inside the estuarine plume in the dry season are weakly stratified in the vertical, with large horizontal salinity gradients near the frontal zone of the plume. Baroclinic dynamics play a very important role in the plume, with the frontal circulation forced by the combination of wind, tides and the Pearl River discharge. In the offshore deep waters of the PRE during the dry season, vertical stratification in the top 15 m is weak and circulation can be approximated by barotropic dynamics forced by wind and tides.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".