Multi-factors reveal spatiotemporal evolution trend and driving mechanism of salinity in coastal zone shallow sea area
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it