Multi-factors reveal spatiotemporal evolution trend and driving mechanism of salinity in coastal zone shallow sea area
Bibliographic record
Abstract
Salinity is a key factor influencing the hydrology, ecology, and biogeochemistry of the ecosystem. Elevated salinity levels can lead to habitat loss. However, most current studies focus on estuarine and coastal wetlands, and the studies on salinity gradient change and its driving mechanism in coastal zone shallow sea areas remain poorly understood. This study selected the temperature and salinity data of Lianyungang in Jiangsu Province, Shidao, and Xiaomaidao in Shandong Province from January 1996 to June 2023. By employing Principal Component Analysis (PCA) and Pearson correlation analysis, we aimed to investigate the spatiotemporal changes and influencing factors of salinity. The results showed that there was no significant difference in the seasonal variation of salinity, which fluctuates around 30‰, and the interannual variation has been decreasing over time. The salinity tended to increase regionally with increasing latitude. Furthermore, both climate and natural factors, such as rainfall and runoff, were found to be crucial drivers of salinity changes, while the impact of proximity to the sea also varied over time. The variation trend of freshwater flux (FWF) and salinity is consistent, and a positive correlation was identified. While it was observed that freshwater input decreases salinity locally. Effects such as ocean currents and human activities, including agricultural land clearing, also regulate salinity. These results provide new insights into the mechanisms driving the spatiotemporal variation of salinity in coastal areas, offer a theoretical foundation for hyper-salinization prevention and control, and highlight directions for future research.
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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 | 0.001 | 0.002 |
| 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".