Spatial pattern analyses of post-fire residual stands in the black spruce boreal forest of western Quebec
Bibliographic record
Abstract
In this study, we characterised the composition and configuration of post-fire residual habitats belonging to two physiographic zones of the black spruce–moss domain in western Quebec. Thirty-three large fires (2000–52 000 ha) were selected and extracted on classified Landsat satellite imagery. The results show that a minimum of 2% and a maximum of 22% of burned areas escaped fire, with an overall average of 10.4%. The many forest patches that partially or entirely escaped fire formed residual habitats (RHs). It was found that although the area of RHs follows a linear relationship with fire size, their proportion appears relatively constant. Spatial analyses showed that the fires could be separated into two groups depending on the physiographic zones (East-Canadian Shield v. West-Clay Belt Lowlands). Fires in the west zone generate less RHs and appear to be associated with more extreme weather conditions. In most cases there was no association with water or wetlands; in some fires the presence of RHs is associated with the proximity of water bodies. The failure to find an association between RHs and wetlands suggests that this type of environment is part of the fuel. Coniferous woodland with moss appears particularly overrepresented within RHs. Our results suggest that the local and regional physiographic conditions strongly influence the creation of RHs; therefore, it is important to consider those differences when applying ecosystem-based management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".