Impact of mountain pine beetle-attacked lodgepole pine logs on veneer processing
Bibliographic record
Abstract
Pilot plant tests and mill trials were conducted to quantify the impact of using mountain pine beetle(MPB)-attacked lodgepole pine (Pinus contorta Dougl.) wood on green veneer processing, and determine if it makes economic sense to sort and process MPB logs separately from normal logs of white SPF (spruce-lodgepole pine-alpine fir) mix for plywood manufacturing. The results demonstrated that log dry-out, improper log conditioning, and veneer peeling contribute to the breakage of veneer ribbon, and in turn, loss of veneer recovery at the green end when processing MPB wood. Compared with the green SPF veneer controls, green MPB veneer has lower moisture content (MC) with smaller variation. The MPB veneer can be clipped narrower with an equivalent of 1% increase in recovery due to less width shrinkage, and be sorted more accurately requiring only two green sorts: heart and light-sap. The MPB veneer can also be dried faster with a reduction in drying time by about 25% for the heart veneer and 35% for the light-sap veneer. However, due to higher volume of narrower random sheets and increased waste from manual handling and composing, the net recovery of the MPB logs is about 8% lower than that of the control SPF logs. Furthermore, the color of the stained MPB veneer is lightened after drying, but it still causes interference with visual grading. Since MPB wood has unique MC and processing characteristics, it is recommended that it be sorted in the log yard when its proportion reaches about 10% of the total logs procured.
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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) | 0.001 | 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) | 0.002 | 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".