Seedbed variation from the interior through the edge of a large wildfire in Alberta
Bibliographic record
Abstract
Despite the importance of seedbeds in the life histories of many plant species, there has been little study of the seedbeds created by wildfire in fire-prone vegetation types such as the boreal forest. Both within the interior and at the edge of a very large (>100 000 ha) 2001 wildfire in the mixedwood boreal region of Alberta, we examined the postfire duff depth and the percent coverage of seedbed types. Minimizing the effect of site and forest composition, we looked only at Picea glauca (Moench) Voss Populus tremuloides Michx. sites burned during a single day of high fire intensity. Good seedbeds (thin humus and exposed mineral soil, with or without ash) averaged 35% coverage within the interior of the fire but varied enormously among stands. There was a weak but significant positive correlation between prefire percent white spruce basal area and percent mineral soil exposure; that is, there is some tendency for conifer stands to create the seedbeds best suited for their own germinants. Fire severity played a clear role in mineral soil exposure, which was greatest in areas with 100% canopy mortality. Mineral soil exposure was far less at the edges of the fire, averaging only 5% even in areas where all trees had been killed; the burn edge was characterized by superficial flaming combustion with no evidence of substantial duff removal via smoldering combustion. In short, the areas where white spruce seed will be most common after the fire, the edges, are where the worst seedbeds in the burn will be found. Regeneration microsites at fire edges appear to be better suited to regeneration of broadleaf species, via suckering; the persistence of white spruce in fire-prone landscapes continues to be difficult to explain.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".