Forecasting wildfire-induced declines in potential forest harvest levels across Québec
Bibliographic record
Abstract
Wildfires are increasing in importance in many regions of the Canadian boreal forest and are an ongoing risk for forest management activities. We simulated the effects of fires on long-term harvest levels on the 59 forest management units of the province of Québec, Canada, for the 2020–2100 period. Different climate change pathways (stable, RCP 4.5 or 8.5) and salvage logging rates (20% or 70% of mature burned stands) were simulated. Changes in forest flammability due to climate change, species migration, and forest management were also considered. Under stable climatic conditions, the decline in potential harvest levels due to fire, based on 50 simulations per scenario, ranged between 3% and 33% (mean = 11%) when high salvage logging rates were simulated, compared to 6%–45% (mean = 20%) for low salvage rates. Climate change caused increases in burn rates between −3% and 39% for RCP 4.5 and between 33% and 69% for RCP 8.5 at the end of the 21st century, depending on fire zones. However, the effects of these modified burn rates on harvest levels did not differ substantially from those of baseline burn rates, probably because the projected burn rates were highest during the later part of the simulations (2070–2100), when their impacts on harvest level calculations were limited. This study indicates that potential harvest levels calculated without considering wildfires are likely to be non-sustainable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".