Assessing planimetric and volumetric coastal changes on Herschel Island Qikiqtaruk, Yukon, Canada
Bibliographic record
Abstract
Permafrost coasts in the Arctic are extremely vulnerable to the effects of global climate change. The increase in sea temperature, the decrease in sea ice extent and longer open-water seasons lead to higher coastal erosion. These erosion rates are among the highest worldwide. This results in significant land loss, both planimetric and volumetric, and leads to a notable reshaping of the coastline. Coastal erosion rates are usually reported in 2 dimensions and focus on the shoreline movement. Few studies have attempted to compute the volumes eroded by coastal retreat. The goal of this study is to connect planimetric and volumetric coastal erosion measurements and to serve as an update of coastal erosion rates in the most recent years on Herschel Island Qikiqtaruk (HIQ) in the Western Canadian Beaufort Sea. LiDAR-derived high-resolution digital elevation models (DEMs) were used to compute volumetric data for the years 2013 and 2023. For the planimetric changes we used digitized coastlines derived from satellite imagery in 2000, 2011 and 2022. Our preliminary results show that the average planimetric erosion ranges between -0.71 and -0.74 [m^2/(m*a)] for the observed periods 2000-2011 and 2011-2022. The volumetric erosion along the entire coastline of Herschel Island Qikiqtaruk experiences a drastic coastal retreat with average values of -33.18 [m^3/(m*a)] whereas the highest values occur along the northwestern, northern and northeastern coastlines of HIQ. This increase can have large implications on the near-shore ecosystems of the island and extensive impacts for the settlement on Herschel Island Qikiqtaruk in the future.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".