Fire regime and old-growth boreal forests in central Quebec, Canada: an ecosystem management perspective
Bibliographic record
Abstract
Boreal forest management in Eastern Canada has caused depletion and fragmentation of old-growth ecosystems, with growing impacts on the associated biodiversity. To mitigate impacts of management while maintaining timber supplies, ecosystem management aims to narrow the gap between natural and managed landscapes. Our study describes the fire history and associated natural old-growth forest proportions and distribution of a 5000 km area located in the black spruce-feather moss forest of central Quebec. We reconstructed a stand-origin map using archival data, aerial photos and dendrochronology. According to survival analysis (Cox hazard model), the mean fire cycle length was 247 years for the 1734–2009 period. Age-class distribution modelling showed that old-growth forests were present on an average of 55% of the landscape over the last 275 years. The mean fire size was 10 113 ha, while most of the burned area was attributable to fires larger than 10 000 ha, leading to old-growth agglomerations of hundreds of square kilometres. In regards to our findings, we propose ecosystem management targets and strategies to preserve forest diversity and resilience.2
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".