Post-Fire Forest Recovery on Sofa Mountain in Waterton Lakes National Park, Alberta, Canada
Bibliographic record
Abstract
Landscape response to disturbance and variable topography is an important topic of research for land managers in fire-prone regions of North America, particularly the mountain west.Waterton Lakes National Park, in southwest Alberta, was the site of a 1998 fire on Sofa Mountain in which 1521 ha of mostly coniferous forests were burned.An investigation of successional growth over the last fourteen years has enabled research to current, emergent vegetation types, and spatial distribution of the mosaic, particularly as associated with topographic factors.This was accomplished with remote sensing, ArcGIS, and ground truthing, allowing a vegetation classification of the burn area which delineates emergent patterns of land cover.Statistical regressions indicated that some vegetative groupings were influenced by specific topographic features, most notably the aspect r-value which was negatively correlated with tree emergence.Slope was the only topographic factor determined to influence the survival of tree patches through a weak negative correlation between slope and surviving trees.Understanding the spatial nature of vegetation regrowth, particularly as associated with topography, can allow land managers to better plan conservation strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".