Recent coastline evolution along the Yukon Coast, western Canadian Arctic
Bibliographic record
Abstract
Soft sediment permafrost coasts are well known for their very dynamic nature. In some places their erosion can reach tens of meters, even though the erosion time is restricted to the short open water season of three to four months per year. Due to its high ground ice content, the Yukon coast in the western Canadian Arctic is particularly prone to erosion. Building on results from Irrgang et al., 2018, we continued analyzing shoreline movements along the Yukon Coast using Pleiades satellite imagery covering the whole Yukon Coast from 2018 and 2022, as well as very highly resolved data from UAV overflights covering long term monitoring sites in 2019 and 2022. Using the Digital Shoreline Analysis System (DSAS) Esri ArcMap extension tool, we quantified shoreline movements for the time periods 2011-2018, and 2018-2022 for the entire coastline and for 2015-2019 and 2019- 2022 for long term monitoring sites. We used the same transects and shoreline proxies as in Irrgang et al., 2018, to ensure comparability of our results and elongate our observation series. We will show how recent shoreline position changes differ from past ones and will provide possible reasoning for these detected changes. We are using our multi-time-step shoreline change rate dataset of the Yukon Coast for training and validation purposes within the Earth Observation for Permafrost Coasts (EO4PAC) project. The increasing usage of machine learning approaches for automated shoreline delineation and shoreline change rate retrieval opens up new pathways – especially if it comes to exploring large and remote areas. Such datasets which contain on site derived shoreline change rates and manually derived shorelines from (very) high resolution airborne and spaceborne data are crucial for training algorithms, validation of results and thus for the quality improvement of machine learning techniques.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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 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".