Predicting sugar maple (<i>Acer</i> <i>saccharum</i>) discoloured wood characteristics
Bibliographic record
Abstract
The presence of wound (strain) initiated discoloured wood columns in the core of sugar maple (Acer saccharum Marshall) stems reduces the proportion of white-coloured wood and, thus, lowers its commercial value. This study aimed to assess the relationship between tree characteristics and the extent and proportion of discoloured wood in sugar maple tree stems. Using 109 trees from three different sites in southern Quebec, we found that the proportion of discoloured wood increased with decreasing sapwood volume and increasing tree age. Younger trees showed a significantly lower proportion of discoloured wood volume. Discoloured wood volume increases disproportionately with tree diameter, while varying among sites. The third important factor affecting the amount of discolored wood was tree vigour as measured by crown characteristics and growth rate changes. A nonlinear mixed-effects model was used to predict discoloured wood taper. Height along the stem was used as a predictor, along with diameter at 1.3 m (DBH), the ratio of live crown length to tree height, and tree height. Although observed injury surface area was positively correlated to discoloured wood volume, injury information did not explain a large share of discoloured wood proportion variation. Overall, older and larger trees with many injuries on less productive sites are likely to have more discoloured wood.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".