Ground surface temperature and humidity, ground temperature cycles and the ice table depths in University Valley, McMurdo Dry Valleys of Antarctica
Bibliographic record
Abstract
Abstract In the upper McMurdo Dry Valleys, 90% of the measured ice table depths range from 0 to 80 cm; however, numerical models predict that the ice table is not in equilibrium with current climate conditions and should be deeper than measured. This study explored the effects of boundary conditions (air versus ground surface temperature and humidity), ground temperature cycles, and their diminishing amplitude with depth and advective flows (Darcy flow and wind pumping) on water vapor fluxes in soils and ice table depths using the REGO vapor diffusion model. We conducted a series of numerical experiments that illustrated different hypothetical scenarios and estimated the water vapor flux and ice table depth using the conditions in University Valley, a small high elevation valley. In situ measurements showed that while the mean annual ground surface temperature approximates that in the air, the mean annual ground surface relative humidity (>85%ice) was significantly higher than in the atmosphere (~50%ice). When ground surface temperature and humidity were used as boundary conditions, along with damping diurnal and annual temperature cycles within the sandy soil, REGO predicted that measured ice table depths in the valley were in equilibrium with contemporary conditions. Based on model results, a dry soil column can become saturated with ice within centuries. Overall, the results from the new soil data and modeling have implications regarding the factors and boundary conditions that affect the stability of ground ice in cold and hyperarid regions where liquid water is rare.
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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 | 0.000 | 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) | 0.001 | 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".