The impacts of anthropogenic disturbance on plant species richness in the freshwater lakes of Algonquin Provincial Park
Bibliographic record
Abstract
This study quantified the impact of human activity on aquatic and shoreline plant species richness. We hypothesized that human activity at the shoreline would negatively impact plant species richness and that the extent of the impact would depend on the intensity of human activity. To test this, we sampled 11 lakes in Algonquin Provincial Park, of which five permitted motorboat access, and five permitted canoe access and prohibited motorboat access. The remaining lake, which had no designated access point for boats and was only accessible to researchers, acted as a control. To assess the impact of anthropogenic disturbance at each lake, we measured plant species richness in three 10 m by 2 m plots: a first plot at the access point, assumed to be the site of highest disturbance; a second at the site of intermediate disturbance, 30 m down shore from the access point; and a third at the site of lowest disturbance, 60 m down shore from the access point. We found a significant negative relationship between the level of disturbance and plant species richness, both in the motorboat-accessible and canoe access-only lakes. The control lake exhibited no correlation between disturbance level and plant species richness. However, there was no significant difference between motorboat-accessible and canoe access-only lakes in the relationship between disturbance level and plant species richness. Overall, this study highlights the consequences of anthropogenic disturbance on freshwater aquatic and shoreline plant communities, and provides a framework for future management and rehabilitation strategies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".