Volume change of tropical Peruvian glaciers from multi-temporal digital elevation models and volume–surface area scaling
Bibliographic record
Abstract
To estimate hydrological storage and better understand the climatic implications of glacier retreat, the volume of glacial ice is a critical but problematic variable. High-accuracy mapping of glacier surface changes over time can directly estimate volume changes, allowing for explicit testing and refining of scaling relationships between changes in glacier volume and surface area that are necessary for making an inventory of remaining glacier mass with remote imagery. A combination of airborne LiDAR, spaceborne remote sensing imagery, digital photogrammetry, and geospatial techniques is used to assess the changes in volume and surface area of six glaciers in the Cordillera Blanca, Peru, between 1962 and 2008. The loss of glacier surface area ranges from 30.79% to 72.62%, corresponding to individual glacier volume changes ranging from 0.019 to 0.150 km3. The volume–surface area scaling is deduced from the change in volume related to the change in surface area by a power relationship quantified from 13 different epoch series. The result shows that there is about 36% more volume loss relative to the loss expected from surface area alone of these individual glaciers in the study area than other glaciers in mid- and high-latitudes from the previous study. Since the error of volume estimation shows a much larger impact on the increase with the size of glaciers, volume–surface area scaling analysis needs to include larger ice masses with regional inventories for a more accurate estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".