Glacier inventory and recent variations of Santa Inés Icefield, Southern Patagonia
Bibliographic record
Abstract
The study of glaciers in remote regions improves our understanding of global glacier change. With an area of 149.31 ± 1.84 km2, the Santa Inés Icefield constitutes one of the largest and least studied and explored glaciated areas of Southern Patagonia. We study the extent and glacier variations of the Santa Inés Icefield over the last 75 years, and we generate the most detailed glacier inventory to date of its 24 constituting glaciers. We estimate surface elevation changes between 2000 and 2014 using Shuttle Radar Topography Mission (SRTM) and TanDEM-X digital elevation models. Our results show a generalized trend of retreat, with a glacier area loss of −9.78 ± 1.52 km2 between 1998 and 2020, with annual rate increase from −0.15 ± 0.01 km2 a−1 (1998–2005) to −0.58 ± 0.10 km2 a−1 (2005–2020), and an average thinning of 0.60 ± 0.26 m a−1 (2σ) between 2000 and 2014. No clear correlation was found between retreat or thinning rates and Accumulation Area Ratio (AAR), terminus slope, aspect, or glacier type. While ERA5 reanalysis data shows no significant climatic trends in temperature or precipitation, a small warming trend below our detection record is the most likely cause of the observed retreat and thinning of the Santa Inés Icefield.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".