A simple system for classifying sugar maple vigour and quality
Bibliographic record
Abstract
In northern hardwood forests, treemarkers are responsible for selecting the trees to be harvested during commercial logging operations. They often select trees based on the presence and severity of defects, information that is used to assign trees to two or more vigour classes (reflecting the risk of dying or declining in growth) and two or more quality classes (reflecting the potential for recovering valuable sawlogs). Using these classes can simplify the tree selection process, but some classification systems may include superfluous classes beyond what is required to explain the variation in tree value. In this paper, we present a parsimonious classification system for sugar maple (Acer saccharum Marsh.) trees using formal classification analyses of empirical product recovery data. We also calculated the net value of trees in each class and assessed which defects affect tree value. Our results show that relatively few defects affect tree value, indicating that treemarkers should use a simpler classification system to describe the vigour and quality of trees. Specifically, we found that the best system contains just three classes: high vigour-high quality, low vigour-high quality, and low vigour-low quality. The use of this simple, easily implemented system would improve the ability of treemarkers to recognize the value of sugar maple trees when selecting the trees to be harvested during commercial logging operations.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".