Eastern white pine regeneration abundance, stocking, and damage along a gradient of harvest intensity
Bibliographic record
Abstract
The shelterwood system is considered appropriate to regenerate Pinus strobus. However, there is a need to quantify the amount of harvesting damage that can be expected relative to the amount of overstory removed during removal harvests and the amount of regeneration that exists prior to harvest. We thus evaluated regeneration response to harvest intensity, as expressed by the percent of basal area harvested. We tested whether regeneration quality and quantity post-harvest increase with decreasing harvest intensity and depend on their pre-harvest abundance. We also posited that soil disturbance increases with harvest intensity. We observed that declines in stocking and density were related to increasing harvest intensity, although most reductions were likely related to skid trails coverage. High pre-harvest densities helped offset some of the losses due to harvesting. A quarter of the regenerating trees alive post-harvest presented some form of damage; the occurrence of damages was not significantly influenced by the percentage of basal area harvested. To ensure regeneration objectives are met, losses during removal harvests should be accounted for; as harvesting intensity increases, losses also increase. Based on these results, minimizing skid trail coverage appears as the most effective way to reduce regeneration losses and damage during shelterwood harvests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".