Nitrogen-retention capacity in a fertilized forest after clear-cutting — the effect of forest-floor vegetation
Bibliographic record
Abstract
Forest fertilization with nitrogen (N) has several benefits to society such as increased wood production and carbon sequestration. There are, however, concerns about N leakage, particularly following clear-cutting. The forest-floor vegetation may increase the N retention of forest ecosystems; however, very few studies have quantified the amount of vegetation required. We studied the relationship between vegetation cover and risk of N leakage, estimated by the amounts of ammonium-N and nitrate-N retained on ion-exchange capsules in the soil, during 4 years following the clear-cutting and harvesting of logging residues in a previously fertilized forest in southern Sweden. Previous fertilization increased the amount of nitrate-N captured on the capsules, whereas the amount of ammonium-N decreased. The vascular vegetation cover increased from almost zero to approximately 25% independent of fertilization. The amount of ammonium-N and nitrate-N retained on the capsules was already reduced by 50%–75% at 20% vegetation cover, and by 30%–40% cover, it approached zero, independent of the number of years since clear-cutting. The vegetation may impede tree-seedling establishment, implying a trade-off between seedling growth and N-retention capacity. However, our results indicate that maximum N retention may be achieved at a relatively low vegetation cover, which could be accomplished with less intrusive scarification methods than currently used.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".