Assessing the Potential Stem Growth and Quality of Yellow Birch Prior to Restoration: A Case Study in Eastern Canada
Bibliographic record
Abstract
Past silvicultural treatments have resulted in the high-grading mixed temperate forests of Québec, Canada. Despite recognition of this issue, the low occurrence of yellow birch (Betula alleghaniensis Britton) within current stands raises questions about the potential of the species to grow and eventually constitute a high-quality forest resource. The objective of this study was to assess this potential using tree characteristics, forest structure and additional site and climatic conditions as predictors. A total of 145 trees were sampled in two areas located in the same bioclimatic zone. Lower-Saguenay-Charlevoix was chosen as an area where a restoration plan could be implemented, whereas Portneuf was selected as a reference. We used nonlinear mixed models to investigate which environmental factors are likely to influence the radial growth and stem quality of yellow birch sample trees. Our results suggest that topographic and climatic conditions, as well as the competitive environment of the trees, are important factors to consider in the evaluation of yellow birch production. Despite the limited occurrence of yellow birch, the potential for growth and quality was high in the Lower-Saguenay-Charlevoix area. For equivalent topographic, climatic, and competitive environment conditions, there was no significant difference in either radial growth or stem quality with Portneuf. We suggest that the economic interest of producing high quality timber should be used to justify the implementation of a restoration strategy in the Lower-Saguenay-Charlevoix area.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".