Litter quality mediated the effect of nitrogen addition and precipitation reduction on the release and immobilization of plant litter nitrogen and phosphorus
Bibliographic record
Abstract
A long-term field litterbag manipulation experiment was conducted to examine the effects of reduced precipitation (−30% of through-fall), nitrogen (N) addition (50 kg N·ha−1·yr−1), and their combination (−30% of through-fall and 50 kg N·ha−1·yr−1) on the release and immobilization of N and phosphorus (P) in four litter types (Pinus koraiensis (PK), Tilia amurensis (TA), Quercus mongolica (QM), and their mixture (MIX)). The results showed that N addition did not significantly stimulate litter decomposition, whereas precipitation reduction and the interaction significantly inhibited litter decomposition. N immobilization was significantly enhanced by N addition and reduced precipitation in the PK, QM, and MIX litters but was significantly inhibited in the TA litter. N addition, reduced precipitation, and their combination significantly increased the final P concentration of the litter in each sampling period. Furthermore, interestingly, there was a significant exponential correlation between the remaining N and final P concentration in the PK litter and a significant linear correlation for the QM and MIX litters, but no significant correlation for the TA litter, indicating that the dynamic relationship between the remaining N and final P concentration in the litter depended on the litter type. These results suggest that the forest litter layer may alleviate the effects of N deposition by increasing litter N immobilization and aggravating soil P limitation by inhibiting litter P release following N deposition. Reduced precipitation may further affect biogeochemical cycles by inhibiting the release of litter N and P.
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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) | 0.001 | 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".