Estimating harvest schedules and profitability under the risk of fire disturbance
Bibliographic record
Abstract
Incorporating fire disturbance into sustainable forest management plans is necessary to provide estimates of variation around indicators for harvest levels, growing stock, profitability, and landscape structure. A fire disturbance model linked to a harvest simulator was used to estimate the probability of harvest shortages under a range of harvest levels and fire suppression scenarios. Results were then used to estimate "sustainable" harvest levels based on a risk tolerance to harvest shortages and the effects of fire suppression. On a 288 000 ha forest in northeastern British Columbia, the cost of historical fire disturbance was estimated at $4 million per year in terms of foregone harvest profits. Suppressing 98.3% of disturbance events to 30% of their historical size had a value of $1.8 million per year. Higher levels of risk tolerance were associated with increased harvest levels and short-term profits, but as timber inventories were drawn down, average long-term profits became volatile. The modelling framework developed here can help to determine resilient forest management strategies and estimate the future flow and variability of harvest volumes, profits, and landscape conditions.
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 | 0.001 | 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 | 0.000 |
| 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".