Impact of Paper Birch (Betula papyrifera) Tree Characteristics on Lumber Color, Grade Recovery, and Lumber Value
Bibliographic record
Abstract
The aim of this research is to assess the impact of paper birch (Betula papyrifera Marsh.) tree characteristics on wood color variability, grade recovery, and lumber value. Current results are based on 2,284 paper birch boards coming from 168 trees harvested in two different stands in Québec, Canada. Results showed that tree diameter was the most important variable affecting board quality and value. Larger trees were associated with higher board quality and higher lumber value per tree. Lumber value per tree was influenced by tree vigor as well but not by tree age. The most vigorous trees produced higher board value with an average of USD 316.62 per m3, middle vigor classes showed averages of USD 218.28 per m3 and USD 251.84 per m3, while the less vigorous trees had the lowest average with USD 165.94 per m3. Board quality was only partly influenced by tree age and tree vigor. When selected for color, the majority of the board surface area fell under the sap category (50%), while 28 percent was classified as regular presenting simultaneously both colorations, and finally only 4 percent of the board area was classified as red. It was found that the most important variables affecting this board color distribution were tree vigor and tree diameter, whereas tree age also had a significant but lesser impact. In general, older, larger, and less vigorous trees tended to present higher proportions of boards classified in the red category. Finally, the results obtained in this study tend to support the practice of silvicultural treatments aiming to produce larger trees yielding higher value and quality boards.
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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 | 0.001 | 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 | 0.000 | 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".