The impact of burn intensity from wildfires on seed and vegetative banks, and emergent understory in aspen-dominated boreal forests
Bibliographic record
Abstract
This paper compares seed and vegetative banks, and the emergent understory in unburned, lightly burned, and intensely burned patches within an aspen-dominated boreal forest in northeastern Alberta, Canada. Propagule banks were measured immediately after the fire, while the understory was surveyed 2 years later. Seedling and shoot emergence techniques were used to assess the abundance and assemblage of species within seed and vegetative banks. Median seed density was ordered unburned > lightly burned = intensely burned patches. A cumulative index of vegetative bank abundance was ordered unburned > lightly burned > intensely burned patches. Species assemblages were significantly different amongst burn intensities for seed banks and emergent understory. Vegetative bank assemblages were significantly different between unburned and burned patches but not between lightly and intensely burned patches. Furthermore, seed and vegetative bank assemblages within each burn intensity were also significantly different. Indicator species analysis suggested that all significant differences were due largely to broad assemblage differences rather than a few unique species. Ordination with nonmetric multidimensional scaling correspondence analysis separated seed and vegetative banks, and emergent understory along two axes (88.8% of the total variation). The first axis (50.3% of the total variation) indicated that the unburned and lightly burned species assemblages were more similar to the vegetative bank, while the intensely burned patches were more similar to the seed bank. The second axis (38.5% of the total variation) placed vegetative banks closer to emergent vegetation than seed banks.Key words: seed bank, bud bank, vegetative bank, aspen, boreal, fire.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".