Implementation of an updated tree marking prescription for selection-managed northern hardwood forests: Effects on stand vigour, quality and value recovery
Bibliographic record
Abstract
In selection-managed northern hardwood forests, tree markers select trees for harvest based on their vigour and quality, which are assessed based on the presence or absence of defects. Recent research has shown that trees that develop crown dieback decline in vigour, but not necessarily in quality, and tree marking simulations indicate that prioritizing the harvest of these high-quality salvage trees increases value recovery by 17–18% compared to existing prescriptions. However, this is likely an overestimate because the tree marking simulations did not account for various operational constraints. We developed an operational tree marking prescription that prioritizes recovery of high-quality salvage trees and conducted a field trial to compare it to two prescriptions commonly used in Ontario. Few high-quality salvage trees were marked under the existing prescriptions, but most were marked under the new prescription, which also retained more high-vigour trees. The new prescription also recovered 15-16% more value, though this difference was not statistically significant. Our results demonstrate that prioritizing high-quality salvage trees increases stand vigour while maintaining or potentially increasing value recovery under operational conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".