Adjusting harvest rules for red oak in selection cuts of Canadian northern hardwood forests
Bibliographic record
Abstract
To enhance the vigour and quality of high-graded hardwood stands, the removal of low-vigour trees is often prioritized during harvesting operations. However, northern red oak (Quercus rubra L.) is rarely affected by defects that are indicative of imminent decline and, therefore, are less likely to be marked for harvesting. Consequently, the red oak volumes harvested during recent years were considerably lower than the estimated annual allowable cut in the public forests of Quebec, Canada. We used data from Quebec's forest inventory to identify variables associated with low-vigour red oak trees. Three groups of explanatory variables were formed to take into account tree size descriptors, inter-tree competition and stand descriptors. Logistic regression revealed that the probability of occurrence of northern red oak of low vigour increased with increasing tree diameter at breast height and dominance. Also, low-vigour oak trees were more likely to be found in stands in which total red oak basal area was low. A cut-point analysis indicated that the maximum diameter threshold for harvesting red oak ranged between 34 and 46 cm. These criteria could help forest managers formulate species-specific tree-marking rules that integrate the need to increase the red oak component in the harvested volume to a level that is closer to the annual allowable cut volume while maintaining stand vigour.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".