Effects of whole-tree harvest on soil-water chemistry at five conifer sites in Sweden
Bibliographic record
Abstract
Logging residues, such as tops and branches, can provide a useful biofuel for large-scale energy production. However, increasing the harvest intensity may affect the soil nutrient stores and water quality. Here, effects on soil-water chemistry after stem-only and whole-tree harvesting were investigated using data from five experimental sites in Sweden, representing medium- to high-fertility sites. They were located in recharge areas on mineral soil and harvested between the years 1995 and 2001. Soil-water samples had previously been collected from below the main part of the root zone in study plots subjected to stem-only or whole-tree harvest. Soil-water chemistry data from the five sites were jointly analyzed by ANOVA using seasonal mean concentrations from the first six seasons after clear-cutting. The concentrations of NO3–-N, K+, and Mg2+ were significantly different (p < 0.05) between the two harvest regimes, indicating lower levels after whole-tree harvest than after stem-only harvest. No significant differences were detected for electrical conductivity, pH, or the concentrations of NH4+-N, Al, Ca2+, SO42–-S, and Cl–. Measurements at one site suggested that the logging residues left on the ground increased the influx of Cl– to the soil. Simple budget calculations indicated that the nutrient export by logging-residue harvest was greater than the export by leaching during the regeneration phase.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".