Long-Term Trampling Effects on Plant Species Biodiversity Along Edmonton’s Ravine Woodlands
Bibliographic record
Abstract
Background Edmonton’s ravine woodlands along the River Valley are rich with riparian vegetation. However, as a recreational spot, it is affected by human trampling, one of the most common forms of disturbances that ravine woodlands experience. Long-term human trampling acts as a factor in spreading non-native plants. Methods Therefore, we examined whether trampling will increase species richness at the edges of the main trail due to the regular introduction of new species. To test this hypothesis, we randomly selected seven transects in flat spots in Kinsmen Park at Edmonton’s River Valley. Then, we created a 4-plot gradient perpendicular to the main trail in each transect and assessed plant species diversity and non-native plant cover in each plot. Results We observed a slight but noteworthy decline in the richness of plant species as distance increased from the main trail, with a p-value of 0.091 indicating marginal significance. However, there was little to no evidence that this decline was due to the decrease in non-native plant cover. Conclusions However, there was little to no evidence that this decline was due to the decrease in non-native plant cover. Long-term trampling has a positive effect on species richness; however, further large-scale studies are needed to investigate the cause of that effect.
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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 | 0.000 | 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) | 0.002 | 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".