Impacts of enhanced nitrogen deposition and soil acidification on biomass production and nitrogen leaching in Chinese fir plantations
Bibliographic record
Abstract
Atmospheric pollution levels in China are increasing quickly. Experience from other polluted regions shows that tree growth could be affected, but long-term effects of N deposition and soil acidification on Chinese forests remain mostly unknown. Soil acidification and N deposition were simulated for Chinese fir ( Cunninghamia lanceolata (Lamb.) Hook.) plantations managed for three consecutive 20-year rotations in southeastern China. A factorial experiment combined four rain pH levels (2.5, 4.0, 5.6, and 7.0), four N deposition rates (1, 7.5, 15, and 30 kg N·ha–1·year–1), and two site qualities (poor and rich). Results indicate that atmospheric pollution effects are not immediate, but after one to two rotations, soil acidification effects could reduce ecosystem C pools significantly (–25% and –11% in poor and rich sites, respectively). N deposition rates above 15 kg N·ha–1·year–1 could offset some of the negative effects of soil acidification and lead to more ecosystem C (19 and 28 Mg C·ha–1 more in poor and rich sites, respectively, than in low N deposition). However, at high N deposition rates (>15 kg N·ha–1·year–1), N leaching losses could greatly increase, reaching 75 kg N·ha–1·year–1. Moderate N deposition could increase tree biomass production and soil organic mass, resulting in increased ecosystem C, but these gains could be associated with important N leaching. Atmospheric pollution could also result in the long term in nutrient imbalances and additional ecological issues (i.e., biodiversity loss, eutrophication, etc.) not studied here.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".