Terrain controls on the occurrence of coastal retrogressive thaw slumps along the Yukon Coast, Canada
Bibliographic record
Abstract
Abstract Retrogressive thaw slumps (RTSs) are among the most active landforms in the Arctic; their number has increased significantly over the past decades. While processes initiating discrete RTSs are well identified, the major terrain controls on the development of coastal RTSs at a regional scale are not yet defined. Our research reveals the main geomorphic factors that determine the development of RTSs along a 238 km segment of the Yukon Coast, Canada. We (1) show the current extent of RTSs, (2) ascertain the factors controlling their activity and initiation, and (3) explain the spatial differences in the density and areal coverage of RTSs. We mapped and classified 287 RTSs using high‐resolution satellite images acquired in 2011. We highlighted the main terrain controls over their development using univariate regression trees model. Coastal geomorphology influenced both the activity and initiation of RTSs: active RTSs and RTSs initiated after 1972 occurred primarily on terrains with slope angles greater than 3.9° and 5.9°, respectively. The density and areal coverage of RTSs were constrained by the volume and thickness of massive ice bodies. Differences in rates of coastal change along the coast did not affect the model. We infer that rates of coastal change averaged over a 39 year period are unable to reflect the complex relationship between RTSs and coastline dynamics. We emphasize the need for large‐scale studies of RTSs to evaluate their impact on the ecosystem and to measure their contribution to the global carbon budget.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".