The Energy and Mass Balance of Peruvian Glaciers
Bibliographic record
Abstract
Abstract Peruvian glaciers are important contributors to dry season runoff for agriculture and hydropower, but they are at risk of disappearing due to climate change. We applied a physically based, energy balance melt model at five on‐glacier sites within the Peruvian Cordilleras Blanca and Vilcanota. Net shortwave radiation dominates the energy balance, and despite this flux being higher in the dry season, melt rates are lower due to losses from net longwave radiation and the latent heat flux. The sensible heat flux is a relatively small contributor to melt energy. At three of the sites the wet season snowpack was discontinuous, forming and melting within a daily to weekly timescale, and resulting in highly variable melt rates closely related to precipitation dynamics. Cold air temperatures due to a strong La Niña year at Shallap Glacier (Cordillera Blanca) resulted in a continuous wet season snowpack, significantly reducing wet season ablation. Sublimation was most important at the highest site in the accumulation zone of the Quelccaya Ice Cap (Cordillera Vilcanota), accounting for 81% of ablation, compared to 2%–4% for the other sites. Air temperature and precipitation inputs were perturbed to investigate the climate sensitivity of the five glaciers. At the lower sites warmer air temperatures resulted in a switch from snowfall to rain, so that ablation was increased via the decrease in albedo and increase in net shortwave radiation. At the top of Quelccaya Ice Cap warming caused melting to replace sublimation so that ablation increased nonlinearly with air temperature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".