Tree marking guidelines for northern hardwoods: a review of criteria for assessing vigour and quality
Bibliographic record
Abstract
Recent studies have highlighted the need to update hardwood tree marking guides by changing the criteria used to assess vigour and quality, and thus the priority for deciding which trees to remove and retain during selection harvests. However, these studies have recommended different criteria, so it remains uncertain which should be included in the classification systems used to assess vigour and quality. We review these studies with the aim of reducing this uncertainty and identifying potential improvements to the provincial tree marking guides for northern hardwood forests in Canada, particularly the Ontario Tree Marking Guide. We review the differences in methodologies and summarize which defects have been shown to affect vigour and/or quality. The defects that should be used to assess vigour are canopy dieback, cankers, and fungi. Decaying wounds, wounds without decay, canopy density, and bark condition could also be used as secondary criteria for borderline cases in which the primary criteria are not decisive. The defects that should be used to assess quality are cankers, fungi, cracks, cavities, and decay (including black bark and wounds with moisture or soft wood). We present a new classification system based on these results and identify potential challenges to its implementation.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".