Characters and evaluation of nitrogen pollution in the water and surface sediment from six urban lakes in Beijing
Bibliographic record
Abstract
The pollution characters,degree and correlations of the indexes in water and surface sediments from six lakes in Beijing were investigated to provide data for the eutrophication control of urban lakes.Results showed that nitrogen pollution of these urban lakes was serious,and the water total nitrogen(TN) of three lakes were all worse than that of National Environment Quality Standard for Surface Water Grade V during the investigation period.According to the Taihu Lake Basin Pollution Standard,the organic nitrogen content(ON%) pollution from the surface sediment of the six lakes were very serious: the minimum from which was three times of the pollution standard level.According to the standard of Canada,the ecological toxic of sediments TN from two lakes were both above serious toxicity standards,while that of other three lakes were all near to the serious toxicity standards.The correlation of nitrogen fractions and TOC content between index of water and surface sediment from the six urban lakes were calculated,and the results showed that the correlation between sediment TN and sediment TOC was the highest(r=0.965,p0.01),correlations between water TN and sediment TN and TOC was extremely significant(p0.01) as 0.960,0.964,respectively.The investigation suggested that the control and reduction of TOC and TN in surface sediment(0~10 cm) of urban lake are of important significance to the control of nitrogen pollution in the lake water.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".