Relevance of stem and crown defects to estimate tree vigour in northern hardwood forests
Bibliographic record
Abstract
Abstract The visual assessment of tree vigour before partial cutting is a key element of the long-term sustainability of managed hardwood forests. Several classification systems have been developed and applied to perform this task during the tree marking process. They segregate trees in different vigour classes based on the presence and severity of stem and crown defects. Yet, the relevance of using these defects to assess tree vigour has rarely been empirically validated. In this study, we analysed the relationships between quantitative vigour indicators and a full range of individual defects using 336 sugar maple and 84 yellow birch trees. Among the tested defects, the tree crown density showed the best ability to identify slow growing, non-vigorous sugar maple trees, regardless of their size. However, none of the stem-related defects, such as bark aspect, the presence of cambial necrosis or conks and stromata were strongly related to our quantitative vigour indicators. For yellow birch, none of the stem defects and crown conditions were found to be related to our vigour indicators. We conclude that, pending longitudinal studies that will provide a full empirical validation of classification systems, crown density should be used to assess recent growth and vigour of sugar maple trees and guide 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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".