Balsam fir and white spruce seedling recruitment in response to understory release, seedbed type, and litter exclusion in trembling aspen stands
Bibliographic record
Abstract
Experimental seeding of balsam fir (Abies balsamea (L.) Mill.) and white spruce (Picea glauca (Moench) Voss) was implemented in three mature trembling aspen (Populus tremuloides Michx.) stands in southeastern Manitoba to test (i) the effect of vegetation (light) competition and seedbed type (undisturbed forest floor, exposed mineral soil, and rotten logs) on seedling recruitment over the first 2 years and (ii) the effect of broadleaf litter exclusion on seedling mortality during the first winter. The study indicated that, with adequate seed supply, seedbed type was the most important factor limiting seedling recruitment, especially the recruitment of white spruce, in trembling aspen stands. Seedling recruitment on the best and the worst seedbeds differed by 1.8 times for balsam fir but by 19 times for white spruce. Significant differences in soil moisture and temperature were found between seedbed types. Broadleaf litter exclusion also facilitated the recruitment of balsam fir and white spruce, but only on undisturbed forest floor. Vegetation (light) competition, however, did not limit seedling recruitment. On the contrary, the presence of understory vegetation benefited seedling recruitment on rotten logs. Compared with white spruce, balsam fir is better adapted to regenerate in trembling aspen stands. Balsam fir was about 4, 12, and 36 times better than white spruce when regenerating on exposed mineral soil, rotten log, and undisturbed forest floor, respectively.
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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 | 0.000 | 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".