Evaluating and Visualizing Drivers of Coastline Change: A Lake Ontario Case Study
Bibliographic record
Abstract
Environmental and climatic changes are disproportionately felt in coastal communities, where drivers of coastline change are complicated with continued development. This study analyzed the coastline change of Lake Ontario in the Town of Lincoln, Ontario, Canada, using a mixed-methods two-phased approach that is novel to the study area. The first phase of the methodology included a coastline change analysis using historical aerial photographs in a geographic information system to identify the most vulnerable sections of the coastline. To better understand the calculated changes, the second phase explored the roles of select climatic and non-climatic drivers of coastline change, such as historic storms and land use changes. The results indicated that four main areas of Lincoln’s coast were more vulnerable, with rates of erosion between −0.32 and −0.66 m/yr between 1934 and 2018. Sections of coastline that had less erosion included those that were more heavily vegetated, attempted a cooperative protection approach, or utilized revetment stones in areas without steep banks. This methodology can help municipalities understand coastline change in a more holistic way to increase their adaptive capacity and allows for the creation of useful visualizations that better communicate to residents and town staff the level of vulnerability of their coasts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".