Long-Term Grazing Effects on Genetic Variability in Mountain Rough Fescue
Bibliographic record
Abstract
Festuca campestris Rydb. (mountain rough fescue) is a dominant grass species in the montane grasslands of western Canada. Little is known about the genetic diversity of this plant and the effects of long-term grazing on the genetics of populations. The amplified fragment length polymorphism (AFLP) technique was applied to compare the genetic diversity of fescue plants at adjacent grazed and protected areas for 3 populations spread across a longitudinal range: Stavely in the foothills, Milroy in the Rocky Mountain trench, and Goose Lake on the interior plateau. Five AFLP primer pairs were used to screen the tiller samples of about 39 plants in each grazed (or ungrazed) area, and 139 polymorphic AFLP bands were scored for each individual sample. These scored bands had frequencies ranging from 0.03 to 0.98 with an average of 0.56. About 81% of the total AFLP variation resided within the populations. The Goose Lake population had the lowest level of AFLP variation, but genetically was the most distinct. Four AFLP bands were possibly associated with chromosomal segments significant for grazing resistance. Comparisons of AFLP variation between grazing and nongrazing samples revealed variable and relatively small impacts of the long-term grazing on the genetic diversity of the grazed populations. The AFLP variation of grazed samples was 1.5% lower at Goose Lake, 2.2% higher at Milroy, and not different at Stavely. If developing diverse germplasm for rangeland seedings is desired, one should sample across geographic space rather than combining materials with and without historical grazing pressure.
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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) | 0.000 | 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".