Spatial distribution of non-point source nitrogen in urban area of Beijing City, China
Bibliographic record
Abstract
Non-point source (NPS) pollution, has been dominant in many urban areas, causing nutrient loss, water body pollution and aqueous systems damaging. Among the pollutants, nitrogen is a key nutrient which can cause eutrophication of rivers, lakes and reservoirs. Beijing, the capital of China, has a booming economy and a huge population. Various human activities have affected nitrogen accumulation deeply and caused considerable NPS pollution. This research calculated the annual load of NPS nitrogen in urban area of Beijing City (UABC) in 2005 and simulated its spatial distribution, which may be useful for environmental planning and pollution control of the UABC and cities with intensive human activities. The total NPS nitrogen load of the UABC was is 1083.09 t in 2005. The load of agricultural land, construction land and unused land were 92.04 t, 969.29 t and 21.77 t. As far as spatial distribution is concerned, construction land was with heavy pollution. Chaoyang district, Haidian district and Fengtai district were the most major export regions of NPS nitrogen loads. The loads of them were 376.88 t, 286.87 t and 249.92 t. As for load intensity of NPS nitrogen, the high-load sources were distributed in the Xicheng district and Dongcheng district, of which the load intensities were 1.10 km 2 /t and 1.11 km 2 /t respectively. Among agricultural land, construction land and unused one, the high-load source was distributed in construction land. Therefore, exhaust gas emission should be reduced, and roof greening as well as road sweep should be improved.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".