Effect of harvest gap formation and thinning on soil nitrogen cycling at the boreal–temperate interface
Bibliographic record
Abstract
In mixedwood forest, different types of commercial thinning that generate different gap sizes are being tested as alternatives to clearcutting to create forest stands with an irregular structure that would emulate the pre-industrial forests. The main goal of this study was to investigate the soil nitrogen (N) dynamics in response to two partial harvesting treatments, used alone or in combination: commercial thinning creating tree-size canopy gaps and harvest gaps creating 0.05 ha gaps. In a 30 year old balsam fir (Abies balsamea (L.) Mill.) stand, both treatments, alone or in combination, as well as unharvested controls were compared on replicated 0.75 ha plots. The most noticeable changes following treatments were observed in gaps, while commercial thinning did not significantly influence any of the parameters assessed. In gaps, increases in N mineralization rates and mineral N concentrations and proportions (NO3−-N and NH4+-N) relative to dissolved organic N were observed. Our results suggest that these changes are caused by the increase in soil temperature and water content. In these forests, the response threshold of the N cycling regarding the size of the intervention would therefore be located between the removal of one or a few trees (one to three stems, 6–12 m2) and a gap of 500 m2. Other studies conducted in different climate and forest types have shown that this threshold could be of equivalent or of a smaller size. These findings will contribute to optimizing our management strategies regarding partial cuts or small-scale clearcuts.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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".