Temporal and Spatial Trends in Soil Moisture in Arctic Alaska
Why this work is in the frame
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Bibliographic record
Abstract
Previous research in the Arctic has demonstrated changes associated with a warming climate including shrub expansion northward, drying of lakes, increasing active layer depths, and decreasing ice and snow cover. With a warming climate, potential for permafrost thaw, increased evapotranspiration from shrubs, and drying lakes, there have likely been widespread changes in patterns of surface soil moisture across the Arctic landscape over the past 20 to 30 years. We investigated trends in soil moisture in Arctic Alaska using the two-decade long data record of ERS-1 and -2 synthetic aperture radar (SAR) satellite data and ground based measurements of precipitation and soil moisture. SAR data have long been known to be highly sensitive to changes in soil moisture condition, and the C-band SAR (~5.6 cm wavelength) of ERS-1 and 2 are particularly useful for monitoring moisture in the low biomass, open ecosystems of the tundra. Eight sites in Alaska, spanning low to high Arctic and coastal to interior tundra, have been used to develop methodologies and relationships between SAR backscatter and soil moisture in tundra ecosystems. Given the dearth of long-term, in-situ soil moisture data, methods have been investigated using surrogate soil moisture information derived from weather station data and the use of the Fire Weather Index (FWI) subsystem of the Canadian Forest Fire Danger Rating System. Previous SAR work in boreal regions has demonstrated high correlations between SAR backscatter at C-band and the drought code (DC) component of the FWI subsystem. DC is a measure of moisture in the deep organic soil layers of 10-20 cm. This paper will present temporal and spatial trends in soil moisture over the two-decade long observation period among the eight study sites. Differences in soil moisture mapping using SAR data between Arctic and boreal systems will be discussed. Recommendations for the use of ERS-1 and -2 data in longitudinal studies will also be highlighted given calibration and data processing issues encountered in this study.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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