Landscape Disturbances Impact Affect the Distribution and Abundance of Exotic Grasses in Northern Fescue Prairies
Bibliographic record
Abstract
Abstract The presence of landscape disturbances increases the establishment of exotic plants inside natural areas. Here, we examine the effect of human disturbances to prairie landscapes on the distribution and abundance of Kentucky bluegrass and smooth brome, exotic grasses that threaten the integrity of prairie ecosystems throughout the northern Great Plains. Using plant inventory data from Riding Mountain National Park in Manitoba, Canada, we investigated how roads, trails, and the intensity of historic livestock grazing affect the distribution and abundance of exotic grasses. On the basis of our results, both Kentucky bluegrass and smooth brome were more abundant in areas closer to roads. Kentucky bluegrass was also more abundant in areas farther from trails and those historically grazed by cattle. Our research demonstrates that the effect of landscape disturbances on exotic grasses varies between species and suggests that patterns of invasion may reflect different introduction histories. Given our findings, conserving the integrity of northern fescue prairies should account for human disturbances of landscapes and their effects on the proliferation of exotic plants into areas of native prairie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".