Engineering Evaluation of Freshwater Lake Coastal Sand Dunes: A Study of Sandbanks Provincial Park, Kingston, Canada
Bibliographic record
Abstract
This study provides a comprehensive engineering evaluation of the freshwater coastal sand dunes at Sandbanks Provincial Park, located on the southern edge of Prince Edward County, Kingston, Ontario, Canada. Sandbanks is renowned for its extensive and dynamic sand dune system, which plays a crucial role in coastal management by absorbing wave energy, mitigating storm surges, and preventing sand deposition on infrastructure. Shaped by historical glacial activities, wind, and wave action, these dunes are critical for stabilizing the beach environment, controlling wind erosion, and supporting diverse plant and animal communities. This research uniquely combines historical data with recent field studies to offer new insights into the engineering properties, geomorphological processes, and ecological dynamics that govern the formation, stability, and resilience of the dunes. The study addresses contemporary challenges such as climate change, human impact, and erosion, and proposes actionable conservation strategies that balance ecological preservation with practical land management. While focused on a specific section of Ontario’s coastal dunes, the findings contribute to a deeper understanding of coastal dune systems more broadly, offering valuable guidance for their long-term sustainability and management across similar environments.
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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 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".