Shoreline Changes in Point Pelee, Canada: An Airphoto-based Analysis
Bibliographic record
Abstract
Shoreline retreat has significant consequences for Point Pelee National Park’s (PPNP) ecological and economic systems. In this study, the shoreline changes of PPNP are examined in terms of their rates, their relationship to natural and anthropogenic influences, and methods for predicting shoreline positions.\nThe rates of shoreline change are analyzed for the period 1959-2004 using ortho-airphotos. The eastern shoreline of the Park exhibited varying recession in both rising and falling Lake Erie water level, especially in the Northeast Beach, where it averaged -3.14 m˖y-1. The western shoreline of the Park generally exhibited accretion and had an inverse relationship with Lake Erie water levels. Longer duration of ice coverage during winter, favourable topography, and sedimentary conditions led to the observed difference in shoreline response.\nUnlike the shoreline net advance observed in 1959-2004, the western shore of PPNP on average exhibited net retreat in the winter of 2005/06. Compared to the winter of 2003/04, changes in Lake Erie water levels, winter wind regime and human-made structures and interferences were negligible during 2004-2006 within the Colchester-to-Southeast Shoal littoral cell in western Lake Erie, where western PPNP is located. However, the winter of 2005/06 exhibited overall low ice cover conditions unlike the typical freeze-up conditions in western Lake Erie. The 2005/06 shoreline retreat on the western shore is attributed to the low winter ice cover conditions during that winter. While less-than-average ice cover does not in its own cause erosion, in certain areas it increases the opportunity for sediment removal by waves by removing a significant source of protection during winter. The episode illustrates the potential impacts of ice-free and low ice cover winters on the western shore of the park and shores under similar conditions.\nAccuracy of predictions of future shoreline positions is important for the park management. Three methods for shoreline position prediction are evaluated: end point rate (EPR), linear regression (LR) and Lake Level Predictor (LLP). On both sides, short-term predictions were more accurate than the longer-term. For eastern and western PPNP, the LR and EPR methods performed best, respectively. The LLP method performed better for the western side, underscoring the relationship between water level and shoreline position. For all methods, the highest errors in prediction were for the northeast PPNP, an area influenced by artificial structures adjacent to the park. This study proposes site-specific method testing before predicting shoreline positions to quantify the errors associated with each method. Human alterations of the sediment budget likely lead to high uncertainty in shoreline positions predictions for affected shores.\nThe study projects shoreline positions in the future for the park’ eastern and western sides. The projections show that between 300,000 and 333,000 m2 of the park’s area will likely be eroded by 2038. In comparison to the park’s area in 1931, by 2038 the park will have diminished by 6.4%. Based on the projection, the majority of the loss will be on the east side and the southern tip from both sides while the rest of the west side will stabilize or slightly advance in some sections. Future climate change patterns are expected to result in shorter ice-durations and more frequent ice-free winters in the western basin of Lake Erie, exposing western PPNP to wave action during the winter season and resulting in increased opportunity for erosion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".