Seasonal variability of surface phytoplankton in the Northern South China Sea: A one-dimensional coupled physical-biogeochemical modeling study
Bibliographic record
Abstract
The South China Sea is an oligotrophic marginal sea located in the tropical-subtropical Northwestern Pacific Ocean. Under the influences of monsoon winds, both the physical and biogeochemical processes exhibit distinct seasonal variability in the upper waters. In order to study the seasonal variations of surface phytoplankton, a one-dimensional coupled physical-biogeochemical model was developed and applied to the deep basins of the Northern South China Sea, away from the coastal upwelling regions. Forced under real-time surface monsoon winds and heat flux, the model reproduced the mixed layer depth, sea surface temperature and surface chlorophyll-a compared with satellite observations and previous reported values. In seasonal mean, the mixed layer depth was highest in winter (∼61.62 m) and lowest in spring (∼12.07 m). The sea surface temperature was lowest (∼25.05°C) in winter and highest (∼29.20°C) in summer. Furthermore, conspicuous phytoplankton blooms occurred in winter with the highest chlorophyll-a concentration up to ∼0.21 mg m−3. In other seasons, the concentration remained relatively low, especially in summer (∼0.05 mg m−3). The spatial distributions of phytoplankton were closely related with patterns of surface nutrient availability, as well as mixed layer depth and sea surface temperature. These relationships indicate that surface phytoplankton primary production was mainly controlled by nutrient availability, which was dominated by vertical turbulent diffusion in the deep basin of the northern Sea which is away from the coastal upwelling regions. Overall, our model results indicated that the seasonal variability of surface phytoplankton was modulated by coupled effects of physical and biogeochemical processes in the Northern South China Sea.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".