Fire, climate change, carbon and fuel management in the Canadian boreal forest
Bibliographic record
Abstract
This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 Fire is the dominant stand-renewing disturbance through much of the Canadian boreal forest, with large high-intensity crown fires being common. From 1 to 3 million ha have burned on average during the past 80 years, with 6 years in the past two decades experiencing more than 4 million ha burned. A large-fire database that maps forest fires greater than 200 ha in area in Canada is being developed to catalogue historical fires. However, analyses using a regional climate model suggest that a changing climate caused by increasing greenhouse gases may alter fire weather, contributing to an increased area burned in the future. Direct carbon emissions from fire (combustion) are estimated to average 27 Tg carbon year–1 for 1959–1999 in Canada. Post-fire decomposition may be of a similar magnitude, and the regenerating forest has a different carbon sink strength. Measurements indicate that there is a net carbon release (source) by the forest immediately after the fire before vegetation is re-established. Daytime downward carbon fluxes over a burned forest take 1–3 decades to recover to those of a mature forest, but the annual carbon balance has not yet been measured. There is a potential positive feedback to global climate change, with anthropogenic greenhouse gases stimulating fire activity through weather changes, with fire releasing more carbon while the regenerating forest is a smaller carbon sink. However, changes in fuel type need to be considered in this scenario since fire spreads more slowly through younger deciduous forests. Proactive fuel management is evaluated as a potential mechanism to reduce area burned. However, it is difficult to envisage that such treatments could be employed successfully at the national scale, at least over the next few decades, because of the large scale of treatments required and ecological issues related to forest fragmentation and biodiversity.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".