Towards accurate quantification of ice content in permafrost of the Central Andes – Part 1: Geophysics-based estimates from three different regions
Bibliographic record
Abstract
Abstract. Increasing water scarcity in the Central Andes due to ongoing climate change recently caused a controversy and debate on the significance of permafrost occurrences for the hydrologic cycle. The lack of comprehensive field measurements and quantitative data on the local variability in internal structure and ground ice content further exacerbates the situation. We present field-based data from six extensive geophysical campaigns undertaken since 2016 in three different high-altitude regions of the Central Andes of Chile and Argentina (28 to 32∘ S). Our data cover various permafrost landforms ranging from ice-poor bedrock to ice-rich rock glaciers and are complemented by ground truthing information from boreholes and numerous test pits near the geophysical profiles. In addition to determining the thickness of the potential ice-rich layers from the individual profiles, we also use a quantitative four-phase model to estimate the volumetric ground ice content in representative zones of the geophysical profiles. Our analysis of 52 geoelectrical and 24 refraction seismic profiles within this study confirmed that ice-rich permafrost is not restricted to rock glaciers but is also observed in non-rock-glacier permafrost slopes in the form of interstitial ice, as well as layers with excess ice, resulting in substantial ice contents. Consequently, non-rock-glacier permafrost landforms, whose role for local hydrology has so far not been considered in remote-sensing-based approaches, may be similarly relevant in terms of ground ice content on a catchment scale and should not be ignored when quantifying the potential hydrological significance of permafrost. We show that field-geophysics-based estimates of ground ice content, while more labour intensive, are considerably more accurate than remote sensing approaches. The geophysical data can then be further used in upscaling studies to the catchment scale in order to reliably estimate the hydrological significance of permafrost within a catchment.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".