Glacier inventory and recent glacier variations in the Andes of Chile, South America
Bibliographic record
Abstract
ABSTRACT The first satellite-derived inventory of glaciers and rock glaciers in Chile, created from Landsat TM/ETM+ images spanning between 2000 and 2003 using a semi-automated procedure, is presented in a single standardized format. Large glacierized areas in the Altiplano, Palena Province and the periphery of the Patagonian icefields are inventoried. The Chilean glacierized area is 23 708 ± 1185 km2, including ~3200 km2 of both debris-covered glaciers and rock glaciers. Glacier distribution varies as a result of climatic gradients with latitude and elevation, with 0.8% occurring in the Desert Andes (17°30′–32° S); 3.6% in the Central Andes (32–36° S), 6.2% in the Lakes District and Palena Province (36–46° S), and 89.3% in Patagonia and Tierra del Fuego (46–56° S). Glacier outlines, across all glacierized regions and size classes, updated to 2015 using Landsat 8 images for 98 complexes indicate a decline in areal extent affecting mostly clean-ice glaciers (−92.3 ± 4.6 km2), whereas debris-covered glaciers and rock glaciers in the Desert and Central Andes appear nearly unchanged in their extent. Glacier attributes estimated from this new inventory provide valuable insights into spatial patterns of glacier shrinkage for assessing future glacier changes in response to climate change.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".