Striving for Sustainability and Resilience in the Face of Unprecedented Change: The Case of the Mountain Pine Beetle Outbreak in British Columbia
Bibliographic record
Abstract
A massive insect outbreak in the public forests of central British Columbia (Canada) poses a serious challenge for sustainable forest management planning. Tree mortality caused by natural disturbances has always been a part of wild and managed forests, but climate change is accentuating the uncertainty around such losses. Policy responses to accelerate overall timber harvesting levels to prevent further tree mortality and to aggressively salvage value from dead wood before it deteriorates can be disruptive and even counter-productive in the long run. Current alternatives are to strategically redirect existing timber harvesting quotas to the most vulnerable areas, minimize overall uplifts in cutting activity, prolong the period over which harvested timber can be processed, avoid the harvesting of mixed species stands or those with good advance regeneration, employ more partial cutting or “selective logging” techniques, and relax standards for acceptable species and inter-tree spacing during post-disturbance stand recovery. At the same time, careful attention to species composition and evolving landscape risk profiles may facilitate adaptation to anticipated climate change and reduce vulnerability to future disturbances. Harvest levels must be set conservatively over the full planning horizon if it is important to assure continuity of the timber supply with few disruptions to regional socio-economics and less stress to ecosystems. Broader lessons in sustainability include the option to emphasize persistence, continuity and flexibility over the long term, though at the expense of maximized production and full resource utilization in the short term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".