Summer recreational boating impacts on erosion, turbidity, and phosphorus levels in Canadian freshwater lakes
Bibliographic record
Abstract
Increases in boat traffic over time can present a risk to the integrity of aquatic ecosystems. In addition to severe environmental degradation or boat-related disturbances, boat wave’s kinetic energy can induce a cumulative impact on freshwater ecosystems. However, seldom data report the impacts of boat waves on shoreline erosion and physico-chemical properties on freshwater lakes. In this study, we monitored shoreline erosion, turbidity, and total phosphorus levels. Wave-induced shoreline erosion was measured through shoreline rebar pin excavation across five lakes in southern Quebec. Sediment resuspension was measured through turbidity sampling in Lake Massawippi over three years. Total phosphorus was also monitored for potential impacts of sediment resuspension. Our study did not detect significant shoreline erosion. However, water turbidity was positively correlated with boat traffic and wind speed and negatively with the littoral slope angle. Moreover, water total phosphorus levels were positively correlated with turbidity. Thus, despite a lack of detectible impact on the shoreline, our results suggest that boat waves can have an impact on freshwater lakes with an increase in sediment resuspension and phosphorus availability. Management agencies could therefore benefit in the long-term from implementing or reinforcing policies aiming to minimize the impact of boats on sediment resuspension.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".