Bibliographic record
Abstract
Abstract. Thermo-erosion gullies (TEGs) are one of the most common forms of abrupt permafrost degradation. They generally form in ice-wedge polygonal networks where the interconnected troughs can channel runoff water. Although TEG can form within a single thawing season, it takes them several decades for their complete stabilization. While the inception of TEGs has been examined in several studies, the processes of their stabilization remain poorly documented, especially the ground ice patterns that form following permafrost aggradation in stabilizing TEGs. For this study, we investigated the impacts of two TEGs in the Canadian High Arctic (Bylot Island, NU, Canada) on ground ice content, cryostratigraphic patterns, and geomorphology to examine permafrost recovery following thermal erosion in ice-wedge polygonal tundra. We sampled 17 permafrost cores from two TEGs – one still active (since 1999) and one stabilized (>100 years old) – to describe the surface conditions, interpret the cryostratigraphic patterns, and characterize the state of permafrost after TEG stabilization. We observed that although the TEG caused discernable cryostratigraphic patterns, ground ice content and active layer thickness of the TEGs were comparable to measurements made in undisturbed conditions. We also noted that once stabilized, TEGs permanently (at the Anthropocene scale) alter landscape morphology and hydrological connectivity. We concluded that although the formation of a TEG has profound effects on the short/medium term and leaves near permanent geomorphological and hydrological scars in periglacial landscapes, on the long term, High Arctic permafrost can recover and return to geocryological conditions similar to those pre-dating the initial disturbance. This suggests that in stable environmental conditions undergoing natural variability, permafrost can persist longer than the geomorphological landforms in which it forms.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.555 | 0.390 |
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".