Vegetation Response of a Dry Mixed Prairie to a Single Spring or Fall Burn
Bibliographic record
Abstract
Climate change may make semiarid grasslands increasingly prone to wildfire. We studied fire seasonality and growing season condition effects on a semiarid grassland in Southern Alberta, Canada. Plots were hand-torched in either fall or spring. Response variables estimated included plant composition and diversity, plant height, aboveground net primary production (ANPP), and forage nitrogen quality. The experiment was replicated over three consecutive growing seasons, and each replicate was monitored for 3 yr thereafter. Drought conditions occurred during two of the six growing seasons. Fall fires appeared to be hotter than spring fires based on a greater fuel mass (standing litter) and exposed the soil surface to a longer period without the benefit of standing litter over winter. Although this grassland is resilient to fire, compared with spring-burned grasslands, the species composition, ANPP, and leaf length of grasses of fall burned communities took a longer time to recover to preburn conditions. Our results suggest that spring-burned grasslands should not be grazed for 1 year post burn to allow time for recovery of ANPP and litter. However, given that ANPP of fall-burned communities also exhibited higher nitrogen concentration that may make the forage more palatable to livestock, and that these communities were more severely impacted, it seems prudent to delay their grazing for more than 1 year to prevent overgrazing. The negative impacts of fire on ANPP may be ameliorated with above-average precipitation in June, which may be forecast during an El Niño year.
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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 | 0.000 | 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".