Life Cycles and Polycyclicity of Mega Retrogressive Thaw Slumps in Arctic Permafrost Revealed by 2D/3D Geophysics and Long‐Term Retreat Monitoring
Bibliographic record
Abstract
Abstract Mega retrogressive thaw slumps (MRTS, >10 6 m 3 ) are a major threat to Arctic infrastructure, alter regional biogeochemistry, and impact Arctic carbon budgets. However, processes initiating and reactivating MRTS are insufficiently understood. We hypothesize that MRTS preferentially develop a polycyclic behavior because the material is thermally and mechanically prepared for subsequent generation failure. In contrast to remote sensing, geophysical reconnaissance reveals the inner structure and relative thermal state of MRTS decameters beneath slump surfaces, potentially controlling polycyclicity. Based on their life cycle development, five (M)RTS were studied on Herschel Island, an MRTS hotspot on the Canadian Beaufort coast. We combine >2 km of electrical resistivity tomography (ERT), 500 m of ground‐penetrating radar (GPR) and annual monitoring of headwall retreat from 2004 to 2013 to reveal the thermal state, internal structure, and volume loss of slumps. ERT data were calibrated with unfrozen‐frozen transitions from frost probing of active layer thickness and shallow boreholes. In initial stage MRTS, ERT displays surficial thermal perturbations a few meters deep, coincident with recent mud pool and mud flow development. In early stage polycyclic MRTS, ERT shows decameter deep‐reaching thermal perturbations persisting even 300 years after the last activation. In peak‐stage polycyclic MRTS, 3D‐ERT highlights actively extending deep‐reaching thermal perturbations caused by gully incisions, mud slides and mud flows. GPR and headwall monitoring reveal structural disturbance by historical mud flows, ice‐rich permafrost, and a decadal quantification of headwall retreat and slump floor erosion. We show that geophysical signatures identify long‐lasting thermal and mechanical disturbances in MRTS predefining their susceptibility to polycyclic reactivation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".