The effects of Livestock Grazing on Vegetation and Lepidopterans in Endangered Alvar Sites in Manitoba's Interlake
Bibliographic record
Abstract
Alvar is a rare type of ecosystem characterized by open, flat terrain and incomplete vegetation intermixed with patches of exposed calcareous pavement. Alvars in Manitoba’s Interlake support tall grass prairie and boreal forest species that do not grow together in any other ecosystem and thus alvars make a unique contribution to biodiversity. The Interlake region is sparsely populated and the dominant land uses are mining and agriculture and livestock grazing is prevalent in alvar areas with thin soils that are not suitable for crop agriculture. Disturbances including grazing may be necessary to maintain alvar plant communities by removing encroaching shrubs and trees, which is beneficial, however grazing may also have negative effects. Studies using bioindicator species to reflect the impacts of disturbance can signal future ecological changes, and indicate areas sensitive to disturbance. I hypothesized that there would be differences in environmental conditions, and in plant and Lepidopteran diversity between grazed and ungrazed sites. Assessment of soil variables showed that soils in grazed sites were significantly more compacted and higher in nitrate and sodium than soils in ungrazed sites. Plant species richness was significantly higher in the ungrazed sites, likely as a result of encroaching forests and soil factors. Ungrazed sites supported a variety of shade-tolerant plant species, while the grazed sites were associated with shade-intolerant and grazing-tolerant/unpalatable species. The butterflies appeared to be more closely associated to the presence of their larval and nectar hosts in alvars, and moths are likely using surrounding forests for food resources. The best management strategy is for grazing to be maintained at a low intensity, and begin later in the season to maximize plant regeneration and access to nectar resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".