Urbanization enhances soil nitrogen mineralization mainly by increasing particulate organic nitrogen fractions in urban park greenspaces: A case study in Hangzhou, China
Bibliographic record
Abstract
In addition to the direct input of inorganic nitrogen (N), the supply of plant-available N driven by soil organic N mineralization is crucial for the development of urban greenspaces, which are essential components of urban ecosystems. Soil N mineralization may vary with urbanization, but the responses of soil N mineralization to different urbanization intensities remain controversial. In this study, we investigated the responses of urban park soil (planted with trees, shrubs or grasses) N mineralization to different urbanization intensities (low, medium and high) in Hangzhou, China. To further evaluate the relative importance of soil organic N fractions in explaining variations in N mineralization, we analyzed the responses of soil particulate organic N (PON) and mineral-associated organic N (MAON) to different urbanization intensities, as well as their relationships with N mineralization. Our results indicated that soil N mineralization increased with increasing urbanization intensity, likely due to increases in soil organic carbon concentration, clay content, microbial biomass and activity under high urbanization intensity. Notably, compared to soil MAON , the increase in soil PON induced by urbanization was more pronounced, and its relationship with soil N mineralization was stronger. Furthermore, soil N mineralization and its relationships with soil organic N fractions varied substantially among different vegetation types . These findings suggest that researchers and urban planners should evaluate the N supply mineralized from soil organic N fractions, particularly PON fractions, to optimize N and vegetation management strategies in urban greenspaces under different urbanization intensities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".