Integrating Laboratory Experiments and Field Instrumentation to Study Storm Impacts on Atlantic Canada’s Coastlines
Bibliographic record
Abstract
Canada’s National Adaptation Strategy highlights that climate change will bring more frequent and intense extreme storms, combined with sea-level rise. Atlantic Canada is especially at risk, as hurricanes generate large waves, storm surges, and coastal erosion that threaten communities and infrastructure. The devastating impacts of Hurricane Fiona in 2022, which caused more than $800 million in damages, revealed how unprepared the region is for future climate-driven extremes. This research project takes steps toward closing that gap by combining laboratory experiments with long-term field monitoring at Martinique Beach, Nova Scotia. In the Queen’s Coastal Engineering Laboratory, I worked with a demonstration flume tank and dyes to capture, through camera-based image processing, how sediments and contaminants move through flowing water. These experiments offered a clear way to see and measure how materials are transported under storm-like conditions. In parallel, I helped prepare and test instrumentation in the large wave basin, including wave buoys and pressure sensors, before they were deployed to the field. The fieldwork centred on setting up a network of monitoring equipment at Martinique Beach: SOFAR Spotter buoys to track offshore wave conditions, Diver piezometers to measure groundwater response, and RBR sensors to record tides and storm surge. This system will operate for the next two years, creating a detailed field site model to monitor beach wave conditions and storm impacts. Through this project, I gained hands-on experience in sensor setup, field preparation, data analysis, and research communication, while working alongside a collaborative team from Queen’s and Dalhousie. The work not only deepened my understanding of storm impacts on coastal systems but also contributed to the broader effort of building resilience for Atlantic Canada’s coastlines in a changing climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".