Quantifying ice wedge volumes in the Canadian High Arctic
Bibliographic record
Abstract
Ice wedges are prominent and precarious features in continuous permafrost environments.As the Arctic regions begin to warm, concern over the potential effects of ice wedge melt-out has become an immediate issue, receiving much attention in periglacial literature.This study estimates the volume of ice wedges over large areas of polar desert in the Canadian High Arctic (Eureka on Ellesmere Island (N80°01', W85°43') and at Expedition Fiord on Axel Heiberg Island (N79°23', W90°59')) through the use of high resolution imagery and the improved capabilities of Geographic Information Systems (GIS) tools.The approach used for this study is similar to that of one performed in Siberia and Alaska by Ulrich et al, (2014).Utilizing the Ulrich et al. technique, this study detected and mapped ice wedge polygons from satellite imagery using ArcGIS.The average width and depth of these ice wedges were obtained from a combination of field observation and data from previous studies at the same location.Furthermore, the assumptions used in the analysis of ice wedge volume have been tested, namely that trough width is representative of ice wedge width, and wedge ice content.Results indicate that the approach used by Ulrich et al, ( 2014) is transferrable to the Canadian High Arctic, and that ice wedge volumes range between 3 -6.6% of the total volume of materials in the upper 6.5 meters.These findings confirm previous studies and their importance is made all the more evident by the dynamic nature of ice wedges where it could be argued that they are a key driver of thermokarst terrain.The expansive prevalence of ice wedges across arctic terrain highlights the importance and the need to improve our understanding of them, as subsidence from ice wedge melt-out could lead to large scale landscape change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".