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 NO 3 -N concentration, NO 3 -N as a proportion of soluble inorganic nitrogen (SIN), N concentration in leachates, N flux, nitrification rates, and pH, but not total N, NH 4 -N, SIN concentration, ammonification, or N mineralization rate. The proportion of soil NO 3 -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 NO 3 -N than removal of groups of trees (i.e., gap creation) and led to smaller increases in NO 3 -N as a proportion of SIN than clearcut harvesting. High levels of retention (>70%) were required to maintain uncut stand N-cycling characteristics. Postharvest NO 3 -N levels could be predicted from NO 3 -N availability in the uncut forests.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".