The Spatial and Temporal Distribution of Dissolved Organic Carbon Exported from Three Chinese Rivers to the China Sea
Bibliographic record
Abstract
The lateral transport of dissolved organic carbon (DOC) plays an important role in linking the carbon cycles of terrestrial and aquatic ecosystems. Neglecting the lateral flow of dissolved organic carbon can lead to an underestimation of the organic carbon budget of terrestrial ecosystems. It is thus necessary to integrate DOC concentrations and flux into carbon cycle models, particularly with regard to the development of models that are intended to directly link terrestrial and ocean carbon cycles. However, to achieve this goal, more accurate information is needed to better understand and predict DOC dynamics. In this study, we compiled an inclusive database of available data collected from the Yangtze River, Yellow River and Pearl River in China. The database is collected based on online literature survey and analysed by statistic method. Overall, our results revealed a positive correlation between DOC flux and discharge in all three rivers, whereas the DOC concentration was more strongly correlated with the regional net primary productivity (NPP). We estimated the total DOC flux exported by the three rivers into the China Sea to be approximately 2.73 Tg yr-1. Specifically, the annual flux of DOC from the Yangtze River, Yellow River and Pearl River was estimated to be 1.85 Tg yr-1, 0.06 Tg yr-1 and 0.82 Tg yr-1, respectively, and the average annual DOC concentrations were estimated to be 2.24 ± 0.53 mg L-1, 2.70 ± 0.38 mg L-1 and 1.51 ± 0.09 mg L-1, respectively. Seasonal variations in DOC concentrations are greatly influenced by the interaction between temperature and precipitation. NPP is significantly and positively related to the DOC concentration in the Yangtze River and the Pearl River. In addition, differences in climate and the productivity of the vegetation may influence both the flux and concentrations of DOC transported by the rivers and thus potentially affect estuarine geochemistry.
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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 | 0.001 | 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) | 0.000 | 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".