Prescribed Burning Has Limited Long-Term Effectiveness in Controlling Trembling Aspen (<em>Populus tremuloides</em>) Encroachment into Fescue Grassland in Prince Albert National Park
Bibliographic record
Abstract
Encroachment into grassland by woody species is a global ecological phenomenon, and it is of particular concern in remnant fescue (Festuca) prairie at the aspen parkland–boreal forest ecotone. Fire suppression is thought to encourage encroachment; however, prescribed burning as a means of controlling encroachment and restoring system structure, function, and composition has had variable success. The objective of this study was to determine the effects of different season of burn, number of annual burns, and number of years after burning treatments on Trembling Aspen (Populus tremuloides) encroachment into the fescue grasslands within Prince Albert National Park, Saskatchewan. Temporal changes in density and cover of Trembling Aspen in grassland and grassland–forest transition plant communities were evaluated using data from a prescribed burn study conducted in Prince Albert National Park from 1975 to 2010. The effect of year (indicating varying amounts of time relative to prescribed burning) and the interaction between spring burning and year reflect a stimulatory effect of burning on Trembling Aspen suckering. Increased Trembling Aspen cover in the forest transition community with more annual burns, burning in the fall, and the interaction between year and number of annual burns and increased Trembling Aspen cover in the grassland community with year indicate that none of the treatments had lasting control of Trembling Aspen encroachment. Ongoing Trembling Aspen encroachment despite prescribed burning may be due to important missing interactions between fire and grazing. A change in the use and expectation of prescribed burning is needed when attempting to suppress Trembling Aspen encroachment into the fescue grasslands.
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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) | 0.000 | 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".