A meta-analysis of the effects of clearcut and variable-retention harvesting on soil nitrogen fluxes in boreal and temperate forests
Bibliographic record
Abstract
One of the assumed advantages of variable-retention (VR) harvesting over clearcut harvesting is reduced postharvest leaching losses of nitrogen. We test this assumption by synthesizing results from long-term field trials in a meta-analysis. Overall, clearcutting significantly increased soil NO3-N concentration, NO3-N as a proportion of soluble inorganic nitrogen (SIN), N concentration in leachates, N flux, nitrification rates, and pH, but not total N, NH4-N, SIN concentration, ammonification, or N mineralization rate. The proportion of soil NO3-N in deciduous forests increased immediately and returned to preharvest levels within five years; the effect was delayed in coniferous forests, but levels remained elevated for several years. Deciduous leaf litter decomposed faster and needle litter decomposed more slowly on clearcut sites than in uncut forests. Single-tree selection caused smaller changes in NO3-N than removal of groups of trees (i.e., gap creation) and led to smaller increases in NO3-N as a proportion of SIN than clearcut harvesting. High levels of retention (>70%) were required to maintain uncut stand N-cycling characteristics. Postharvest NO3-N levels could be predicted from NO3-N availability in the uncut forests.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.026 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".