New results of the very high-resolution UAV surveys of the Yukon coast: Border to King Point
Bibliographic record
Abstract
Climate models indicate the highest warming rates for the high latitudes, especially for the Arctic. Recent estimates indicate that the release of previously frozen organic carbon and its transformation into greenhouse gases may push global climate warming above the 1.5 °C targeted in the COP21 Paris Agreement (Schuur et al., 2015). Despite efforts to include carbon fluxes from permafrost degradation in climate models, the lateral fluxes of organic matter from land to sea are still not accounted for (Vonk and Gustafsson, 2013). Arctic permafrost coasts are major carbon (Schuur et al., 2015) and mercury pools (Schuster et al 2018) and represent about 34% of Earth’s coastline, with large sectors affected by significant erosion rates (Fritz et al, 2017). Year-round reduction in Arctic sea ice is forecasted and by the end of the 21st century, models indicate a decrease in sea ice area ranging from 43 to 94% in September and from 8 to 34% in February (IPCC, 2014). An increase of the sea-ice free season duration will expose coasts to wave action, extending the erosion into the shoulder seasons. Changing climate will also modify the contribution of terrestrial erosion, e.g. thermokarst, gully erosion and retrogressive thaw slumps (Fritz et al., 2015, Ramage et al 2017, 2018, Irrgang et al 2018). Understanding the current processes and both inter- and intra-annual dynamics of coastal erosion in the Arctic is essential to better predict future coastal erosion rates and hence to improve carbon and contaminant flux estimates. \nFollowing previous research by the Geological Research of Canada and the Alfred Wegener Institute, in July-August 2018, we resurveyed several long-term monitoring sites from the Canada-US border to King Point: Border, Clarence, Nunaluk, Herschel’s slumps A, B, C, D and Tina’s, Stokes West, Kay Point and King Point. Traditionally the repeat surveys were conducted using a DGPS survey along fixed transects that cross-cutted each site. In 2018, we have partially repeated the DGPS surveying and surveyed all sites with a SenseFly RTK ebee UAV with a S.O.D.A. camera and a Trimble R4 base station, allowing for preliminary model accuracies of ci. 10 cm. The poster shows the results of the 2018 surveys and first comparisons with data from previous seasons, including a discussion of the main results and methodological adjustments that may be needed for the 2019 surveys. This research is integrated in the H2020 European Union project Nunataryuk - Permafrost thaw and the changing Arctic coast, science for socioeconomic adaptation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".