Managing the Economic Impacts of Mountain Pine Beetle Outbreaks in Alberta
Bibliographic record
Abstract
Output from the SELES MPB Landscape Scale Mountain Pine Beetle Model (Fall et al., 2004) was utilized to estimate potential mountain pine beetle spread rates within the Hinton Wood Products Forest Management Area (HFMA) of the Foothills Model Forest. From the SELES model output three spread rate scenarios were hypothesized. Scenario 1 hypothesized a Mountain Pine Beetle (MPB) spread rate slower than the rate estimated by the SELES MPB Model. Within Scenario 1, current Annual Allowable Cut (AAC) levels were hypothesized to be adequate to harvest MPB damaged lodgepole pine stands. Scenario 2 hypothesized that spread rates would be consistent with the recommended run from the SELES MPB Model, resulting in attack of the majority of the stands within the HFMA within 29 years. Scenario 3 hypothesized that spread rates would be higher than estimated by the SELES MPB Model, resulting in attack of the majority of lodgepole pine stand in the HFMA in 20 years (Scenario 3.1) or 10 years (Scenario 3.2). The even flow harvest rates required to utilize commercially viable stands attacked by MPB were determined (Surge Period). The modeling program Forest Muncher was utilized to estimate the decrease in AAC which could result from succession / salvage harvest of the majority of lodgepole pine stands within the HFMA within each scenario (Post Surge Period). Based on these AAC estimates, the potential economic impact of MPB attack influenced AAC changes was examined utilizing output from the Computable General Equilibrium Framework (CGE) Model developed by Mike Patriquin and Bill White of the Canadian Forest Service (Patriquin et al., 2005). Scenario 1 had a nearly inappreciable impact on the economic indicators for the forest industry or the total economy in the Foothills Model Forest Area. Within Scenario 2, forest industry revenue, royalties, labour income, and employment were estimated to increase by 40 – 50% during the Surge Period and decrease by 4.7– 6.0% in the Post Surge Period. Within Scenarios 3.1 and 3.2 forestry revenue, royalties, labour income and employment increases ranged from 70 – 90% for Scenario 3.1 and ranged from 160 – 210% for Scenario 3.2 during the Surge Period. Revenue, royalties, labour income and employment in the forest industry were estimated to decrease by 6 – 9% within the Post Surge Periods of Scenarios 3.1 and 3.2. Economic, forest industry capacity, social and environmental factors which may limit the feasibility of large scale salvage of mountain pine beetle damaged stands are discussed within the report.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".