Response of chlorophyll-a to rainfall event in the basin of the South China sea: Statistical analysis
Bibliographic record
Abstract
Chlorophyll-a (Chl-a) is an essential ecological indicator, and affected by processes such as typhoons, mesoscale eddies, and Rossby waves. However, the impact of more frequent and widespread precipitation events on Chl-a seems to be overlooked. This study utilized remote sensing data and reanalysis data to investigate the response of Chl-a to 240 precipitation events in the central South China Sea from 2005 to 2019. The results indicate that precipitation events have a significant impact on Chl-a concentration. Following a precipitation event in 2019, the Chl-a concentration in the affected area increased by approximately 0.22 mg m-³ from the 3rd to the 7th day. The reasons for the increase in Chl-a concentration were the vertical mixing induced by wind stirring and the upwelling caused by wind stress curl, which transported nutrients to the euphotic zone, lowering the sea surface temperature and triggering a proliferation of phytoplankton. Additionally, dissolved nutrients in precipitation provided a nutrient source for Chl-a growth. The contributions of nutrient supply, wind speed, and wind stress curl to the increase in Chl-a concentration during precipitation events were 18%, 37%, and 45%, respectively. Precipitation events enhanced marine primary productivity, playing a crucial role in deepening our understanding of ocean-atmosphere interactions and their impact on marine ecosystem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".