Climate change, caribou protection, and Canada's timber supply
Bibliographic record
Abstract
Managed forests are a significant contributor to Canada's economic wealth. However, forestry activities increase landscape fragmentation and impact wildlife species, such as Canada's woodland caribou, that depend on large areas of undisturbed habitat. Proposed conservation policies for caribou in Canada aim to retain 65% or more of caribou ranges as undisturbed landscapes, which would help achieve a 60% likelihood of self-sufficiency of caribou populations. This level of habitat protection may require moving some forest areas out of industrial forestry use and into habitat protection. We have assessed the extent to which this level of range protection would affect timber supply to forest mills in Canada at present-day harvest levels. For the six largest Canadian provinces (British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, and Quebec), we solved an optimization problem that allocated harvest sites across the industrial forestry zone to forest mills at present-day harvest levels with and without caribou conservation and under present and future climate conditions. Retaining 65% of each caribou range area under protection generated moderate timber supply reductions in Quebec and Alberta, with smaller reductions in British Columbia. Sensitivity analyses revealed modest timber supply shortages in Ontario, Saskatchewan, and Manitoba at range retention levels as high as 75%–80%. The estimated timber supply shortages from implementing caribou conservation measures were similar to, or smaller than, those resulting from climate change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".