Remote sensing evaluation of High Arctic wetland depletion following permafrost disturbance by thermo-erosion gullying processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Northern wetlands and their productive tundra vegetation are of prime importance for Arctic wildlife by providing high-quality forage and breeding habitats. However, many wetlands are becoming drier as a function of climate-induced permafrost degradation. This phenomenon is notably the case in cold, ice-rich permafrost regions such as Bylot Island, Nunavut, where degradation of ice wedges and thermo-erosion gullying have already occurred throughout the polygon-patterned landscape resulting in a progressive shift from wet to mesic tundra vegetation within a decade. This study reports on the application of the normalized difference vegetation index to determine the extent of permafrost ecosystem disturbance on wetlands adjacent to thermo-erosion gullies. The analysis of a GeoEye-1 image of the Qarlikturvik valley, yielding a classification with five classes and 62% accuracy, resulted in directly identifying affected areas when compared to undisturbed baseline of wet and mesic plant communities. The total wetland area lost by drainage around the three studied gullies approximated to 95 430 m 2 , which already represents 0.5% of the total wetland area of the valley. This is worrisome considering that 36 gullies have been documented in a single valley since 1999 and that permafrost degradation by thermal erosion gullying is significantly altering landscape morphology, modifying wetland hydrology, and generating new fluxes of nutrients, sediments, and carbon in the watershed. This study demonstrates that remote sensing provides an effective means for monitoring spatially and temporally the impact of permafrost disturbance on Arctic wetland stability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it