Effects of substrate availability and competing vegetation on natural regeneration of white spruce on logged boreal mixedwood sites
Bibliographic record
Abstract
Given a seed source, the quality of available substrates is a key factor in determining the success of white spruce (Picea glauca (Moench) Voss) natural regeneration. We examined the influence of substrate and competing vegetation on survival and growth of natural regeneration of white spruce up to 4 years following harvesting in deciduous-dominated upland boreal mixedwood sites. Feather moss, thick soil surface organic layers, litter, and solid wood were poor substrates for establishment. Early successional mosses establishing on mineral soil, thin organics, and rotten wood were generally favourable microsites but were not highly available on postharvest sites. Mineral soil substrates were not as suitable as expected, likely because on a postlogged site, they are associated with unfavourable environmental characteristics (e.g., low nutrient availability, exposure). There was some evidence that survival and growth of seedlings were improved by surrounding vegetation in the first years, but heavy competing vegetation had a negative impact on older seedlings. Burial by aspen litter greatly increased seedling mortality, especially when combined with a brief period of submergence due to heavy spring snowmelt. The results provide insight into conditions under which natural regeneration could be an option for establishing white spruce following harvesting of deciduous-dominated boreal mixedwood forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".