Effects of nitrogen deposition on litter decomposition and nutrient release mediated by litter types and seasonal change in a temperate forest
Bibliographic record
Abstract
We conducted a field experiment with four levels of simulated nitrogen (N) deposition (0, 2.5, 5, and 7.5 g N m−2 yr−1, respectively) to investigate the response of litter decomposition of Pinus koraiensis (PK), Tilia amurensis (TA), and their mixture to N deposition during winter and growing seasons. Results showed that N addition significantly increased the mass loss of PK litter and significantly decreased the mass loss of TA litter throughout the 2 yr decomposition processes, which indicated that the different responses in the decomposition of different litters to N addition can be species specific, potentially attributed to different litter chemistry. The faster decomposition of PK litter with N addition occurred mainly in the winter, whereas the slower decomposition of TA litter with N addition occurred during the growing season. Moreover, N addition had a positive effect on the release of phosphorus, magnesium, and manganese for PK litter and had a negative effect on the release of carbon, iron, and lignin for TA litter. Decomposition and nutrient release from mixed litter with N addition showed a non-additive effect. The mass loss from litter in the first winter and over the entire study correlated positively with the initial concentration of cellulose, lignin, and certain nutrients in the litter, demonstrating the potential influence of different tissue chemistries.
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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) | 0.001 | 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 | 0.000 |
| 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".