Variability of Wood Color in Paper Birch in Québec
Bibliographic record
Abstract
Color variability of paper birch (Betula papyrifera Marsh.)wood at the tree level was examined in this article.Tree age, dimension, and vigor were expected to influence the proportion of discolored wood in paper birch boards; older, larger, and less vigorous trees were assumed to produce boards with higher proportions of discolored wood.The color analysis was performed on approximately 2250 boards produced from 168 paper birch trees harvested in two different stands from which only logs of sawing quality were used.An industrial scanner was used to digitize the boards and obtain colorimetric information.Results show that tree diameter and vigor significantly influenced the proportion of discolored wood in boards, whereas the effect of tree age did not have a significant influence in the model.An average area of 32.4% of discolored wood was obtained when considering all boards.Less vigorous trees showed a mean area of 45.32%, whereas middle-vigor and most-vigorous trees had mean areas of 30.78 and 15.47%, respectively.The colorimetric values were mainly affected by tree age and diameter, but these effects were variable for every colorimetric parameter.The analysis of the random effects demonstrated that most of the total random variance of the dependent factors came from the between-board, between-tree, and, to a lesser extent, between-log variation.These findings suggest that favoring shorter rotations would help produce trees with lower proportions of discolored 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".