The distribution and dynamics of aufeis in permafrost regions
Bibliographic record
Abstract
Abstract Aufeis, also known as an icing or naled, is an accumulation of ice that forms primarily during winter when water is expelled onto frozen ground or ice surfaces and freezes in layers. Process‐oriented aufeis research initially expanded in the 20th century, but recent interest in changing hydrological conditions in permafrost regions has rejuvenated this field. Despite its societal relevance, the controls on aufeis distribution and dynamics are not well defined and this impedes projections of variation in aufeis size and distribution expected to accompany climate change. This paper reviews the physical controls on aufeis development, current broad‐scale aufeis distribution and anticipated change, and approaches to aufeis investigation. We propose an adjustment to terminology to better distinguish between the formation process and resulting ice bodies, a clarification of the aufeis classification approach based on source water, and a size threshold for broad‐scale aufeis inventory to facilitate collaborative research. We identify additional objectives for future research including advancing process knowledge at fine spatial scales, describing broad‐scale distribution using current remote sensing capabilities, and improving our understanding and predictive capacity over the interactions between aufeis and landscape‐scale permafrost, hydrogeological, geotectonic, and climate conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".