Characteristics and Evaluation for Nitrogen Pollution in Water and Surface Sediments of Xixi Wetland
Bibliographic record
Abstract
In this paper, it was investigated and evaluated about characteristics of nitrogen pollution for 11 sampling water or surface sediments (0~10cm) in Hang Zhou Xixi Wetland, and correlation analysis was conducted amongst nitrogen indexes for water and that for surface sediments. The results revealed that nitrogen nutrient loading was generally serious in water of Xixi Wetland, total nitrogen(TN) and NH4+-N concentration exceeded the standard in most sampling water. Organic nitrogen(ON) was the mainly existing nitrogen forms in surface sediments of Xixi Wetland, according to evaluation criteria for organic nitrogen (ON, %) in sediments, the content of organic nitrogen (ON, %) in all the sampling sediments exceeded the pollution level IV, and TN contents showed serious biological toxicity in most sampling site based on Ontario environmental quality standards. Significantly positive correlation was found between concentration of TN or NH4+-N in water and NO3--N content in surface sediments, moreover, content of NO3--N in surface sediments may be attributed to the decomposition of organic matter in sediments since significantly negative correlation was found between total organic carbon (TOC) content and NO3--N in sediments. Therefore, It is of great significance for controlling and reduction of organic matter and TN content existed in surface sediment of Xixi Wetland.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".