Bibliographic record
Abstract
Lake Huron is a popular tourist destination and is home to several businesses and residents. Since the shoreline is dynamic and is subject to change over the years due to several factors such as a change in water level, soil type, human encroachment, etc., these locations tend to encounter floods due to increased water levels and wind speed. This causes erosion and loss to the properties along the shoreline.\nThis study is based on two areas of interest named Pinery Provincial Park and Sauble Beach which are located on the shoreline of Lake Huron where Pinery Provincial Park is a naturally maintained shoreline and Sauble Beach is altered by humans to make it a tourism-oriented beach. The project investigates and compares the changes in shorelines between both locations to study the effects of two different shoreline maintenance practices. The change is then further studied by adding a dimension of water levels from 1970 to 2021 and future level changes. A software application named Digital Shoreline Analysis System (DSAS) version 5.0 was used within ESRI’s ArcMap 10.8 to perform shoreline change analysis. DSAS produces results in the form of the following statistics: shoreline change envelope (SCE), net shoreline movement (NSM), endpoint rate (EPR), linear regression rate (LRR), and weighted linear regression (WLR). EPR was used to analyze shoreline change rate and NSM was used to map flooding and erosion hazards.\nThis project also examines the areas which may flood in the future due to climate change and unprecedented water level rise by using the following two approaches: a) Hypothetical situation was considered in which there would be ±2m water level change on top of forecasted water levels by US Army Corps of Engineers and Fisheries and Oceans Canada and b) Built-in Kalman Filter Model in DSAS was used to predict the shoreline for next 10 and 20 years. Based on these approaches, flood and erosion hazard maps were created considering variables such as water level, slope, elevation, and bathymetry. After analysis, a 3D model was created to showcase the areas which could be impacted based on the first approach in future flooding scenarios. The analysis is accompanied by the study of shoreline management strategies commonly used in Canada and based on the results of the analysis, recommendations for future management strategies will be made to minimize the impact of the flood. Lastly, the overall results of Sauble Beach and Pinery Provincial Park are compared and discussed in section 6.\nThe results indicate that the Pinery Provincial Park shoreline has a stable shoreline compared to Sauble Beach. Pinery Provincial Park is having about 50% fewer erosion rates and negative shoreline movement than Sauble Beach. It cannot be neglected that both study areas are facing increased erosion and decreased accretion over the years but the human interference and the Sauble Beach municipality’s neglecting towards sustainable tourism practices has resulted in losing its beach at a higher rate. This project suggests adapting green shoreline management techniques to both the study area. These results may be useful for the authorities, local government agencies, and NGOs that are tasked with developing and implementing shoreline management plans.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.033 | 0.006 |
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".