Using salvage logging and tolerance to risk to reduce the impact of forest fires on timber supply calculations
Bibliographic record
Abstract
It is acknowledged that natural forest fires cannot and even should not be eliminated from the North American boreal forest. Forest fires produce immediate losses of wood volume, disrupt the conversion of the actual forest age structure into a target structure, and prevent planned timber supply (PTS) levels from being achieved. In this paper, we explore the extent to which periodic shortfalls in available timber under various burn rates can be mitigated through salvage logging and the tolerance of forest managers to a given level of shortfall, both as a function of forest age class structure. Simulations are done using both a deterministic and a stochastic representation of burn rate over time. Results show that the frequency of shortfall events can be reduced by salvage logging and by the introduction of measures that generate a tolerance to shortfall and that this mitigation potential is influenced by initial forest age class structure and burn rate. Results also show that even a 100% rate of salvage logging cannot fully compensate for timber losses to fire and eliminate fire-induced timber shortfalls. Furthermore, interannual burn rate variability reduces the efficiency of both mitigation measures. As the PTS is never realized under fire risk, the real cost of opting for different PTS scenarios should be estimated not from the difference in PTS but rather from the more realistic difference in realized timber harvest.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".