Regeneration development under shelterwoods in a lowland red spruce – balsam fir stand
Bibliographic record
Abstract
The shelterwood system can be used to establish regeneration and to improve the growing conditions of seedlings and, thereby, increase their probability of survival after final harvest. To determine the cutting intensity that best promotes the development of red spruce ( Picea rubens Sarg.) regeneration, an experiment, including four repetitions of five treatments (control; low, moderate, and heavy partial cuttings; and clear-cutting), was established in a lowland stand in Quebec, Canada, dominated by red spruce and balsam fir ( Abies balsamea (L.) Mill.). Regeneration development responses to treatments varied according to cutting intensity, species, and position relative to the skidding trails in the residual stands. Ten years after treatment, red spruce seedlings were well established but were smaller than those of balsam fir and deciduous species. Red spruce seedling height was generally greater in partial-cut skidding trails, as was light availability. However, in clearcuts, the size of red spruce seedlings established in skidding trails seemed to be negatively affected by the considerable quantity of woody debris strewn over them. Among the partial-cut treatments, tree regeneration leaf biomass was the highest where 60% of the initial basal area was removed. Thus, this treatment is an attractive alternative to clear-cutting in such lowland stands where watering-up is anticipated after final harvest.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".