Effects of previous nitrogen fertilization on soil-solution chemistry after final felling and soil scarification at two nitrogen-limited forest sites
Bibliographic record
Abstract
Nitrogen (N) fertilization and soil scarification are common measures used in commercial forestry in the boreal zone. This study was performed to investigate how previous N fertilization in two N-limited Scots pine (Pinus sylvestris L.) stands affected the soil-solution chemistry after final felling and also to determine the effect of subsequent soil scarification. Nitrogen had been applied to study plots at different intervals, resulting in total applications of 0, 450, 900, or 1800 kg N·ha−1. Soil-solution samples were collected before and after whole-tree harvesting of the P. sylvestris stand, from undisturbed soil and also after harvesting from soil below furrows, tilts, and areas between furrows created by disc trenching. After harvesting, the K+ concentration was lower at higher N fertilization intensities. No overall effect on the N concentrations was detected. Electrical conductivity and the concentrations of Na+, K+, Mg2+, Ca2+, Cl−, NO3−–N, total N, and total C were all affected by soil scarification. The highest concentrations of these variables were found below tilts and the lowest concentrations below furrows. The experiment was repeated, at a lower monitoring intensity, at a site where the previous total N application amounted to 0 and 450 kg N·ha−1. Here the NO3−–N concentration responded to disc trenching in a similar way to that observed in the main experiment. The study shows that previous N fertilization of N-limited forest does not necessarily affect the soil-solution chemistry significantly after whole-tree harvesting.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".