Long-term effects on soil-water nitrogen and pH of clearcutting and simulated disc trenching of previously nitrogen-fertilised pine plots
Bibliographic record
Abstract
Forest fertilisation with nitrogen (N) typically increases N leaching for 1–2 years. Some studies have reported effects also after clearcutting. This study presents an analysis of soil-water chemistry data from the 3rd to the 15th year after clearcutting of fertilised experimental plots on a low-fertility site in Sweden. Before clearcutting in 1987, study plots had been fertilised with NH4NO3 in 1967, 1974, and 1981, resulting in total applications ranging from 0 to 1800 kg N·ha−1. In 1989, disc trenching was simulated by manual digging on small subplots within the fertilised main plots. Soil-water samples were collected at a depth of 50 cm. Previous N fertilisation and site preparation, respectively, affected (p < 0.05) the total N and NO3–-N concentrations and pH of soil water, but no statistical interaction between fertilisation and site preparation was found. The NO3–-N concentration was elevated for total N applications above 720 kg·ha−1 (mean NO3–-N concentration of 0.93 mg·L−1 for 1080 kg N·ha−1, 1.6 mg·L−1 for 1440 kg N·ha−1, and 2.4 mg·L−1 for 1800 kg N·ha−1 compared with 0.20 mg·L−1 for the control) and lower after simulated disc trenching (0.63 mg·L−1) than in nontrenched soil (1.3 mg·L−1). The elevations in the soil-water NO3–-N concentration for the fertiliser treatments seemed to be related to changes in the soil N store created by previous fertilisation.
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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) | 0.001 | 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 | 0.001 |
| 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".