Costing the production of OSB using fibre impacted by mountain pine beetle
Bibliographic record
Abstract
The current outbreak of mountain pine beetle (MPB) in western Canada has killed millions of hectares of pine forest, but has created the opportunity for oriented strand board (OSB) manufacturers to utilize this forest resource. However, MPB - killed timber is dry, porous and brittle, thus posing significant challenges at various stages of the OSB production process. These challenges include increases in breakage in timber harvesting, production of fine wood material at the stranding operation, wear on stranding blades, and resin use. In this study, a simulation of a western Canadian woodlands and OSB manufacturing process was constructed using the operations based costing framework. Once the simulation was constructed, expected impacts of MPB fibre were introduced to the model in a hypothetical scenario where a mixture of MPB and deciduous fibre were used in the manufacturing process to determine the overall cost impact of the alternative furnish mixture on cost of production. This analysis showed that for the subject operations, the use of a 28.8% mixture of MPB and deciduous furnish would yield 3.6% overall increase in the cost of finished OSB. To eliminate this cost gap, OSB manufacturers could investigate the economic viability of increasing ponds capacity, increasing ponds temperature, sale of excess fines material and alternative resination processes to allow inclusion of fines in finished product. --P. ii.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".