Arctic Report Card 2020: Coastal Permafrost Erosion
Bibliographic record
Abstract
Since the early 2000s, observations from 14 coastal permafrost sites have been updated, providing a synopsis of how changes in the Arctic System are intensifying the dynamics of permafrost coasts in the 21st Century. Observations from all but 1 of the 14 permafrost coastal sites around the Arctic indicate that decadal-scale erosion rates are increasing. The US and Canadian Beaufort Sea coasts have experienced the largest increases in erosion rates since the early-2000s. The mean annual erosion rate in these regions has increased by 80 to 160 % at the five sites with available data, with sites in the Canadian Beaufort Sea experiencing the largest relative increase. The sole available site in the Greenland Sea, on southern Svalbard, indicates an increase in mean annual erosion rates by 66 % since 2000, due primarily to a reduction in nearshore sediment supply from glacial recession. At the five sites along the Barents, Kara, and Laptev Seas in Siberia, mean annual erosion rates increased between 33 and 97 % since the early to mid-2000s. The only site to experience a decrease in mean annual erosion (- 40%) was located in the Chukchi Sea in Alaska. Interestingly, the other site in the Chukchi Sea experienced one of the highest increases in mean annual erosion (+160%) over the same period. In general, a considerable increase in the variability of erosion and deposition intensity was also observed along most of the sites.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.025 |
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".