Wildfire Severity, Recovery, and Grazing Management in the Dry-Mixed Grasslands of Southern Alberta and Saskatchewan
Bibliographic record
Abstract
In the fall of 2017 two wildfires in southern Alberta and Saskatchewan burned approximately 28 000 hectares under extreme weather conditions. These fires completely burned over many ranches, and raise many questions, including how the fire severity and recovery are affected by topographic and soil gradients, and how biomass production and plant species diversity recover including the role of grazing management decisions in recovery. Fire severity in relation to slope, aspect, and available fuel was assessed utilizing the bare soil index (BSI) by calculating the difference between the amount of soil exposure from pre-fire to immediately after the fires. Recovery of biomass production in relation to fire severity, land capabilities, potential land productivity, and solar heat load was assessed utilizing the normalized difference vegetation index (NDVI) to compare post-fire vegetative greenness to that of baseline pre-fire peak biomass greenness. Recovery of live biomass and species metrics with and without fire and grazing were assessed using a factorial randomized complete block design. I found that fire severity increased with increased slope and decreased vegetative greenness. Fire severity was highest in areas with slopes steeper than 15 and aspects that were within the 45 flanks of the dominate wind direction. Recovery of biomass was best in areas of moderate fire severity and solar heat load. The complete recovery of live biomass was noted by year three of the study and the complete recovery of litter was not noted by year five. Grazing has no significant effect on recovery of either biomass or species metrics.
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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 | 0.001 | 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 | 0.000 |
| 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".