Wake Up - Impacts of Recreational Boat Wakes on Shoreline Habitat in the Rideau Canal Waterway
Bibliographic record
Abstract
Growth in pleasure craft ownership and the emerging popularity of wake-enhancing boats have raised concerns that freshwater and riparian ecosystems may be under increased stress from recreational boating. Boat wakes, the series of waves generated by a moving boat, can have variety of potentially harmful effects on shoreline environments, including shoreline erosion, increased turbidity, habitat disturbance, and impacts to biological communities. The relative impact of boat wake energy is most pronounced in low-fetch environments where wind wave energy is limited, such as rivers and canals. We present preliminary results of a boat wake impact study conducted on the Rideau Canal Waterway (RCW), one of Canada’s most popular destinations for recreational boating. The study has two components: 1) a boat traffic survey to quantify the types and abundance of recreational boats in use on the RCW; and 2) experimental trials with different types of boats run at known speeds and distances from shore. In both study components, near-shore wave fields and turbidity were measured by RBRmaestro3 multi-channel logger to correlate boat wakes to sediment resuspension (a proxy indicator of erosion). Wakes were characterized by peak and significant wave heights and analyzed via continuous wavelet transforms. Sediment resuspension was inferred from turbidity data and correlated to boat wakes by time series analysis. The results will contribute to developing predictive models of shoreline impacts based on boat type, speed, and distance from shore that can be used to inform management action on the RCW and other Canadian waterways
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".