An Examination of Seasonal Mean Circulation and Salinity Distributions in the Pearl River Estuary of China Using a Nested-Grid Coastal Ocean Circulation Model
Bibliographic record
Abstract
A three-level nested-grid coastal ocean modeling system was developed recently by Tang et al. (2009) for the Pearl River Estuary (PRE) in South China's Guangdong Province. The modeling system has three downscaling subcomponents: (a) a coarse-resolution outer model for China Seas from Bohai Sea to the northern South China Sea; (b) an intermediate-resolution middle model for coastal waters over the northern shelf of South China Sea; and (c) a fine-resolution inner model for the PRE and adjacent waters. The modeling system is forced by tides, meteorological forcing and buoyancy forcing associated with freshwater runoff from the Pearl River system. Multi-year model results in 1993–95 are used in this study in examining circulation and salinity distributions during the dry (December–March) and wet (May–August) seasons in the PRE. The seasonal mean circulation and salinity distributions in the dry season are affected significantly by freshwater runoff, wind and tides over the northern and western PRE and mainly by wind and tidal forcing over the outer PRE and adjacent inner shelf waters. In the wet season, the estuarine plume extends significantly offshore with river discharge and tides to be the main driving for circulation and salinity distributions inside the PRE, and tides and wind forcing to be the main driving forcing over the inner shelf waters off the PRE.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".