Evaluation of a tree classification system in relation to mortality risk in Québec northern hardwoods
Bibliographic record
Abstract
A tree classification system was developed in the 1980s as part of a guide for tree-marking in the rehabilitation of unevenaged northern hardwood stands in Québec. It differentiates trees that are at high and low risk of mortality, trees with sawlog potential and cull trees. The risk class was assessed based on the presence of major crown and bole defects. The main objective of the present study was to evaluate this system with respect to its capacity to predict the probability of tree mortality. The variables used to classify the trees were observed in 88 experimental plots (0.5 ha) established between 1983 and 1999. Tree-level mortality probabilities were modelled for sugar maple (Acer saccharum Marsh.), American beech (Fagus grandifolia Ehrh.) and yellow birch (Betula alleghaniensis Britt.) to test the significance of the classification variables. The presence of decay, fungus or canker, wounds, uprooting, the death of at least 30% of the crown or of the roots, and the product class had significant (p < 0.05) effects on mortality probabilities for at least one of the 3 species studied. In the main, the results supported the tree classification system. However, this system could be modified to differentiate not only trees with a high or low mortality risk, but also to identify some very high-risk trees. Key words: northern hardwood, mortality, defect, quality, classification, selection cutting, partial cut, sugar maple, American beech, yellow birch, uneven-aged, tree-marking
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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 | 0.002 | 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".