Relationships between prefire composition, fire impact, and postfire legacies in the boreal forest of Eastern Canada
Bibliographic record
Abstract
Canadian mixedwood forests have a high compositional and structural diversity. It includes both hardwood (aspen, balsam poplar, and white birch) and softwood (balsam fir, white spruce, black spruce, larch, and white cedar) species that can form pure stands or mixed stands. This heterogeneity results in a variety of vertical structural strata that can potentially interact with fire behaviour. Fourteen fire impact maps including information on preburn stand composition and structure were gathered in a Geographical Information System. The relative influence of prefire forest composition, stand density, and surficial deposits on postfire forest cover attributes (such as variation in proportion of green/red/charred trees) was analyzed using contingency tables. Many attributes of postfire forests (fire legacy) can be related to preburn forest composition and structure. Highest fire impact was observed in coniferous stands. At the other end of the spectrum, aspen stands and wetlands contributed to most of the fire skips. Within coniferous stands, there was a difference between species with regard to their susceptibility to windthrow following fire. Jack pine stands had less severe windthrow allowing for an abundance of snags, whereas windthrow is common in balsam fir stands. Impacts vary with regard to fire severity, suggesting that observed differences between stand types may be less important when fires are very intense. These results have consequences on the maintenance of the diversity of the forest mosaics through time as well as our capability to predict fire behaviour and impacts.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".