Ice-wedge polygons distribution, morphometry and state in the Tombstone Territorial Park, Central Yukon, Canada
Bibliographic record
Abstract
Ice wedge (IW) polygons form through thermal contraction induced by winter cooling of ice-rich permafrost which results in the formation of cracks. Hoar frost develops in the cracks in winter and meltwater infills the cracks during spring and freezes. As the cracking and infilling occurs repeatedly, IWs grow, leading to characteristic surface morphology with depressions or troughs aligned on the axis of the IW and raised rims or ridges on either side. Surface expression of IW is either characterized as low-centered polygons or high-centered polygons, the former being associated with the first stages of IW development, and the latter with IW degradation. Because IWs represent important excess ice close to the surface, considerable local subsidence and related effects on landscape parameters, such as vegetation and moisture, are likely to occur upon degradation. IW polygons distribution, morphometry and state were characterized in the Tombstone Territorial Park (Central Yukon, Canada) using semi-automated remote sensing techniques, field observations and laboratory analyses. The data is used to define determining landscape factors for IW polygons occurrence, to characterise the stages of the IWs development and/or degradation and to estimate the volume of buried ice in the region. Results show that elevation, slope and material are important elements defining IW polygons distribution. The relationship between landscape factors and stages of development is not as clear, and, despite climate changes being homogenous in the area, IW development and degradation is very heterogenous, as shown by the differing moisture, greenness and brightness signals across the polygonal terrain.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".