Using spatially explicit simulations to explore size distribution and spacing of regenerating areas produced by wildfires: recommendations for designing harvest agglomerations for the Canadian boreal forest
Bibliographic record
Abstract
It is now recognized that in the Canadian boreal forest, timber harvesting activities have replaced wildfires as the main stand-replacing disturbance. Differences in landscape patterns derived from these two sources of disturbance have, however, raised concerns that the way forest harvesting has been dispersed is potentially shifting patterns away from the natural range. In the context of natural disturbance-based management, we used a spatially explicit model designed to capture general fire regimes in order to quantify temporal variability associated with regenerating areas (burnt areas of 25 years or younger), and to develop strategic objectives for harvest agglomeration sizes and dispersion. We first evaluated temporal variability in the proportion of stands younger than 100 years (assumed to be even-aged stands) for various fire regimes (seven fire cycles: 50 to 400 years, and three mean fires sizes: 3000, 15 000 and 60 000 ha). Secondly, we quantified the size distribution and dispersion of regenerating areas for each fire regime. As expected by theoretical fire frequencies and size distributions, the importance of even-aged stands at the forest management unit level was found to decrease with longer fire cycles. However, the temporal variability associated with these proportions is shown to increase with mean fire size. It was also observed that the size distribution and dispersion of regenerating areas was primarily influenced by mean fire size. Based on these observations, natural disturbance-based management objectives were formulated, providing guidelines on harvest agglomeration size and dispersion. Key words: temporal variability, boreal forest, fire regime, forest management, age distribution, fire size distribution, clearcut agglomeration size distribution
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".