Defoliation impacts on Festuca campestris (Rydb.) plants exposed to wildfire
Bibliographic record
Abstract
Wildfires commonly occur in the Fescue Prairie of Alberta, but little information exists to provide a basis for making grazing recommendations after burning. A wildfire in April 1999 provided an opportunity to study the effect of season and intensity of post-burn defoliation on foothills rough fescue (F. campestris Rydb.) in southwestern Alberta. A 3 (date of defoliation) x 2 (defoliation intensity) factorial experiment with 10 replicates (plants) was established in both a burned and a non-burned grassland and analyzed as a nested design. Plants were defoliated once during active vegetative growth (17 May), inflorescence development (2 July), or dormancy (30 September), at either 5 or 15-cm clipped stubble heights in the first growing season after fire. Burning increased tiller numbers by 54% compared to non-burned plants but reduced plant ANPP by 51% in the second growing season. While a single defoliation of burned plants, particularly early in the year, had little effect on growth, delaying defoliation into July decreased tillers 1 year later. Increasing defoliation intensity had the greatest impact on non-burned plants, reducing plant height (15%) as well as tiller (21%) and plant (32%) ANPP in the second year. May defoliation reduced etiolated growth 1 year later regardless of burn treatment. A single grazing event after wildfire does not necessarily appear to detrimentally affect rough fescue; however, the low herbage available immediately after fire may not justify the increased risk to the plant with subsequent grazing.
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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".