Computed tomography (CT) scanning of internal log attributes prior to sawing increases lumber value in white spruce (<i>Picea glauca</i>) and jack pine (<i>Pinus banksiana</i>)
Bibliographic record
Abstract
The increased pressure on timber supply due to a reduced forest land base urges the development of new approaches to fully capture the value of forest products. This paper investigates the effects of knowing the position of knots on lumber volume, value, and grade recoveries in curve sawing of 31 white spruce (Picea glauca (Moench) Voss) and 22 jack pine (Pinus banksiana Lamb.) trees. Internal knot position was evidenced by X-ray computed tomography (CT) imaging, followed by the application of a knot-detection algorithm allowing log reconstruction for use as input in the Optitek sawing simulation software. Comparisons of the three levels of sawing optimization (sweep up, shape optimized, and knot optimized) revealed that considering internal knots before log sawing (e.g., knot optimized) generated 23% more lumber value for jack pine and 15% more for white spruce compared with the traditional sweep-up sawing strategy. In terms of lumber quality, the knot-optimized strategy produced 38% more pieces of grade No. 2 and better in jack pine and 15% more such pieces in white spruce compared with the sweep-up strategy. These results indicate a great potential to increase manufacturing efficiency and profitability by implementing the CT scanning technology, which should aid in developing a strong bioeconomy based on an optimized use of 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".