Coastal Hazard Vulnerability Assessment: A Case Study of Erosion and Flooding on Herschel Island, Yukon Territory, Canada \n
Bibliographic record
Abstract
Coastal infrastructure, cultural, and archeological sites are increasingly vulnerable to erosion and flooding along permafrost coasts. Amplified warming of the Arctic, sea level rise, lengthening of the open water period, and a predicted increase in frequency of major storms compound these threats. Mitigation necessitates decision-making tools at an appropriate scale. We present a study of coastal erosion combining it with a flooding risk assessment for the culturally important historic settlement on Herschel Island, a UNESCO World Heritage candidate site. The resulting map may help local stakeholders devise management strategies to cope with rapidly changing environmental conditions. \n \nWe analyzed shoreline movement using the Digital Shoreline Analysis System (DSAS) after digitizing shorelines from 1952, 1970, and 2011. Using these data, forecasts of shoreline positions were made for 20 and 50 years into the future. Flooding risk was assessed using a cost-distance map based on a high-resolution Light Detection and Ranging (LiDAR) dataset and current Intergovernmental Panel on Climate Change sea level estimates. \n \nWidespread erosion characterizes the study area. The rate of shoreline movement for different periods of the study ranges from -5.5 to 2.7 m·a-1 (mean -0.6 m·a-1). Mean coastal retreat decreased from -0.6 m·a-1 to -0.5 m·a-1, for 1952-1970 and 1970-2000, respectively, and increased to -1.3 m·a-1 in the period 2000-2011. Ice-rich coastal sections, and coastal sections most exposed to wave attack exhibited the highest rates of coastal retreat. The geohazard map resulting from shoreline projections and flood risk analysis indicates that most of the area occupied by the historic settlement is at extreme or very high risk of flooding, and some buildings are vulnerable to coastal erosion. \n \nThe results of this study indicate a greater threat by coastal flooding than erosion. Our assessment may be applied in other locations where limited data are available.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".