Threats to Coastal Infrastructure from Erosion and Flooding: A Case Study from Herschel Island, Yukon Territory, Canada
Bibliographic record
Abstract
Arctic coastal infrastructure, cultural, and archeological sites are increasingly vulnerable to erosion and flooding due to amplified warming of the Arctic, sea level rise, lengthening of open water periods, and a predicted increase in frequency of major storms. Mitigating these hazards necessitates decision-making tools at an appropriate scale. The objectives of this study were to assess potential erosion and flood hazards at Herschel Island, a UNESCO World Heritage candidate site, and produce a map to be used as a decision making tool. The study focused on Simpson Point and the adjacent coastal sections, because of their archeological, historical, and cultural significance. \nShoreline movement was analyzed using the Digital Shoreline Analysis System (DSAS) after digitizing shorelines from 1952, 1970, 2000, and 2011. For purposes of this analysis, the coast was divided in seven coastal reaches (CRs) reflecting different morphologies and/or exposures. Using linear regression rates obtained from these data, projections of shoreline position were made for 20 and 50 years into the future. Flood hazard was assessed using a least cost-path analysis based on a high-resolution Light Detection and Ranging (LiDAR) dataset and current Intergovernmental Panel on Climate Change sea level estimates. \nWidespread erosion characterizes the study area. The rate of shoreline movement in 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 most exposed to wave attack exhibited the highest rates of coastal retreat. The geohazard map combines shoreline projections and flood hazard analyses to show that most of the spit area has extreme or very high flood hazard potential, and some buildings are vulnerable to coastal erosion. \n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".