Strategic analysis of forest vulnerability to risk related to fire: an example from the coniferous boreal forest of Quebec
Bibliographic record
Abstract
As fire is a major disturbance in boreal forests, it is now recognized that it has to be taken into account in forest management planning. Moreover, as the time of exposure to fire is related to stand productivity, combining information on productivity and fire should help in assessing the potential to sustainably manage forests. We present a method to assess potential vulnerability to the risk of fire and illustrate it in the boreal coniferous forest of Quebec. This method takes into account some sources of uncertainty related to the estimation of productivity and fire risk. Spatialization of stand productivity from growth and yield curves allowed us to compute the area comprised of productive stands of each district with or without considering fire risk. Results showed that productive area is generally decreasing with decreasing degree-days, increasing elevation, or in relation to surficial geology. Furthermore, districts with moderate to good productivity were found to be vulnerable to fire when burn rates were greater than 0.333%·year–1. Our innovative approach allowed us to assess the vulnerability of the districts to fire and could be helpful in many regions in the context of a projected increase in future area burned under climate change.
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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".