Thaw slump inventory and TanDEM-X elevation loss rates (2015): Tuktoyaktuk coastlands, Canada, and Lena Delta area, Sakha Republic, Russia
Bibliographic record
Abstract
Predicting future thaw slump activity requires a sound understanding of the atmospheric drivers and geomorphic controls on mass wasting across a range of time scales. On sub-seasonal time scales, sparse measurements indicate that mass wasting at active slumps is often limited by the energy available for melting ground ice, but other drivers such as rainfall or the formation of an insulating veneer are also thought important. To study the sub-seasonal drivers, we derive topographic changes from single-pass radar interferometric data acquired by the TanDEM-X satellite (12 m resolution). The high vertical precision (around 30 cm), frequent observations (11 days) and large coverage (5000 km²) allow us to track volume losses as drivers such as the available energy change during summer in two study regions. We find that thaw slumps in the Tuktoyaktuk coastlands, Canada, are not energy limited in June, as they undergo limited mass wasting (height loss of around 0 cm/day) despite the ample available energy, indicating the widespread presence of an insulating snow or debris veneer. Later in summer, height losses generally increase (around 3 cm/day), but they do so in distinct ways. For many slumps, mass wasting tracks the available energy, a temporal pattern that is also observed at coastal yedoma cliffs on the Bykovsky Peninsula, Russia. However, the other two common temporal trajectories are asynchronous with the available energy, as they track strong precipitation events or show a sudden speed-up in late August, respectively. The observed temporal patterns are poorly related to slump characteristics like the slump area. The contrasting temporal behaviour of nearby thaw slumps highlights the importance of complex local and temporally varying controls on mass wasting.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".