Formation and evolution of buried snowpack deposits in Pearse Valley, Antarctica, and implications for Mars
Bibliographic record
Abstract
Abstract Buried snowpack deposits are found within the McMurdo Dry Valleys of Antarctica, which offers the opportunity to study these layered structures of sand and ice within a polar desert environment. Four discrete buried snowpacks are studied within Pearse Valley, Antarctica, through in situ observations, sample analyses, O-H isotope measurements and numerical modelling of snowpack stability and evolution. The buried snowpack deposits evolve throughout the year and undergo deposition, melt, refreeze, and sublimation. We demonstrate how the deposition and subsequent burial of snow can preserve the snowpacks in the Dry Valleys. The modelled lifetimes of the buried snowpacks are dependent upon subsurface stratigraphy but are typically less than one year if the lag thickness is less than c. 7 cm and snow thickness is less than c. 10 cm, indicating that some of the Antarctic buried snowpacks form annually. Buried snowpacks in the Antarctic polar desert may serve as analogues for similar deposits on Mars and may be applicable to observations of the north polar erg, buried ice at the Mars Phoenix landing site, and observations of buried ice throughout the martian Arctic. Numerical modelling suggests that seasonal snows and subsequent burial are not required to preserve the snow and ice on Mars.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".