Distribution and activity of ice wedges across the forest‐tundra transition, western Arctic Canada
Bibliographic record
Abstract
Abstract Remote sensing, regional ground temperature and ground ice observations, and numerical simulation were used to investigate the size, distribution, and activity of ice wedges in fine‐grained mineral and organic soils across the forest‐tundra transition in uplands east of the Mackenzie Delta. In the northernmost dwarf‐shrub tundra, ice wedge polygons cover up to 40% of the ground surface, with the wedges commonly exceeding 3 m in width. The largest ice wedges are in peatlands where thermal contraction cracking occurs more frequently than in nearby hummocky terrain with fine‐grained soils. There are fewer ice wedges, rarely exceeding 2 m in width, in uplands to the south and none have been found in mineral soils of the tall‐shrub tundra, although active ice wedges are found there throughout peatlands. In the spruce forest zone, small, relict ice wedges are restricted to peatlands. At tundra sites, winter temperatures at the top of permafrost are lower in organic than mineral soils because of the shallow permafrost table, occurrence of phase change at 0°C, and the relatively high thermal conductivity of icy peat. Due to these factors and the high coefficient of thermal contraction of frozen saturated peat, ice wedge cracking and growth is more common in peatlands than in mineral soil. However, the high latent heat content of saturated organic active layer soils may inhibit freezeback, particularly where thick snow accumulates, making the permafrost and the ice wedges in spruce forest polygonal peatlands susceptible to degradation following alteration of drainage or climate warming.
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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 | 0.001 | 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) | 0.001 | 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".