Mountain pine beetle, global markets, and the British Columbia forest economy
Bibliographic record
Abstract
A number of near-term timber supply shocks are projected to impact global forest product markets, particularly mountain pine beetle induced timber reductions, a Russian log export tax, and timber supply increases from plantation forests in the Southern Hemisphere and Sweden. We examined their effect on a number of global jurisdictions using a dynamic global forest products trade model that separates British Columbia (BC) into coastal and interior forest sectors. The results suggest that global increases in plantation timber would have negligible effects on BC log and lumber markets, that the Russian tax would have minor effects on this market, and that the beetle-induced timber supply drop would moderately increase BC prices (primarily log prices). In the United States South, lumber and log prices could rise as a result of the mountain pine beetle, while other shocks will have a negligible impact on prices. Yet, lumber production will fall because log prices will increase substantially more than lumber prices. Japan could be impacted much more than other regions by the Russian tax on log exports. In the absence of export taxes, a beetle-induced timber shortage would cause lumber production in Japan to rise (as Japan can access nearby Russian logs), while the export tax would reduce lumber production because log prices rise disproportionately more than in other regions.
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".