Mitigating the mid-term timber supply gap : potential silvicultural solutions in the Prince George TSA
Bibliographic record
Abstract
The current mountain pine beetle epidemic (Dendroctonus ponderosae Hopkins) (MPB) has affected 17.5 million hectares in British Columbia (BC). In response, the chief forester raised the annual allowable cut (AAC) in several Timber Supply Areas (TSAs) throughout the province, including the Prince George TSA. The AAC uplifts were designed to allow licensees to harvest MPB mortality which would otherwise be worthless if left standing. This had the unwanted consequence of creating a mid-term (11-50 years from now) timber supply gap that will result in lowered AAC’s and associated job loss and economic decline. This paper explores three silvicultural systems that could increase the volume of timber available for harvest in the mid-term. The three systems are: understory retention, pre-commercial thinning (PCT), and fertilization. To determine the potential of these systems to increase mid-term harvest volumes I conducted a literature review and used the Table Interpolation Program for Stand Yields modeling program (TIPSY). Understory retention has some potential to increase volumes, but only by a small amount. The benefits of understory retention are ecological in nature, and include increased biodiversity and forest resilience. PCT and fertilization, either separate or combined, have good potential to increase mid-term harvest volumes. For PCT and fertilization to be effective, it is critical to select appropriate stands. The minimum height requirements for what is considered suitable understory should be lowered. This will result in less stands being salvage harvested, and will increase the ecological diversity and resilience of pine-leading stands. There may also be some associated increases in mid-term harvest volumes. PCT and fertilization should be used in conjunction in stands that will benefit most from these treatments. This will increase available mid-term harvest volumes. If done properly, the risk associated with spending money on silvicultural treatments is acceptable.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".