Biochar mitigates dissolved organic carbon loss but does not affect dissolved organic nitrogen leaching loss caused by nitrogen deposition in Moso bamboo plantations
Bibliographic record
Abstract
Dissolved organic matter (DOM) is one of the most reactive and mobile components in terrestrial ecosystems. Frequent loss of DOM has a negative effect on the surrounding environment. Current abundant deposition of atmospheric nitrogen (N) could significantly influence DOM leaching, and biochar has been suggested to be applied for improving acidic soils However, it remains unclear whether biochar affects dissolved organic carbon (DOC) or dissolved organic nitrogen (DON) loss induced by N deposition in acidic soils. In this study, we observed the effects of biochar amendment (BC0: 0 t biochar ha−1, BC20: 20 t biochar ha−1, and BC40: 40 t biochar ha−1) on the leaching of soil DOC and DON in Moso bamboo plantations that received simulated N deposition (N30: 30 kg N ha−1 yr−1, N60: 60 kg N ha−1 yr−1, N90: 90 kg N ha−1 yr−1, and N-free) for 34 months. DOC loss showed a marked seasonal variation with the lowest loss occurring in spring and the largest loss in summer; no such trend was observed in DON loss. Nitrogen deposition generally increased DOC and DON leaching loss in all four seasons, except DOC leaching loss in spring. Biochar amendment significantly decreased DOC leaching loss in spring, autumn, and winter; however, there was no significant effect on DON. Biochar may therefore mitigate DOC and DON loss caused by N deposition. DOC loss mitigation was also greater than that for DON, especially in autumn. Biochar application is a potential approach to mitigate the DOC and DON leaching loss induced by increasing atmospheric N deposition in Moso bamboo plantations.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".