Fire severity and plant productivity recovery in a mixed grass prairie wildfire driven by extreme winds
Bibliographic record
Abstract
Background Wildfire on rangelands in the mixed grassland can severely disrupt livestock operations. Understanding how fire severity impacts post-fire production recovery is important for grazing management. Aims We examined how topography and other environmental factors influence wildfire severity, or the consumption of biomass and exposure of soil, under extreme (>120 km h−1) wind conditions in native mixed grass prairie in western Canada. We also examined how variation in fire severity impacts grassland production recovery. Methods Fire severity and production recovery were measured using the bare soil index (BSI) and normalised difference vegetation index (NDVI). Impacts of topography, wind exposure, and site capability on fire severity and production recovery were assessed using generalised additive models. Key results Fire severity varied as a function of slope, wind exposure and fuel load. Severity peaked at NDVI between 0 and 0.4, values associated with high litter content and minimal green vegetation. Interactions between slope and aspect with respect to dominant wind direction generated very high fire severity on slopes greater than 15° that faced into the wind. Production recovery increased moderately with higher fire severity and recovery was generally higher on sites with lower potential productivity. Implications Post-fire production recovery was rapid; fire severity and site capability had only modest impacts on recovery rates demonstrating the resilience of grassland ecosystems to even severe wildfire.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".