Glacier ablation and temperature indexed melt models in the Nepalese Himalaya
Bibliographic record
Abstract
Abstract Temperature index (TI) models are convenient for modelling glacier ablation since they require only a few input variables and rely on simple empirical relations. The approach is generally assumed to be reliable at lower elevations (below 3500 m above sea level, a.s.l) where air temperature (Ta) relates well to the energy inputs driving melt. We question this approach in High Mountain Asia (HMA). We study in-situ meteorological drivers of glacial ablation at two sites in central Nepal, between 2013 and 2017, using data from six automatic weather stations (AWS). During the monsoon, surface melt dominates ablation processes at lower elevations (between 4950 and 5380 m a.s.l.). As net shortwave radiation (SWnet) is the main energy input at the glacier surface, albedo (α) and cloudiness play key roles while being highly variable in space and time. For these cases only, ablation can be calculated with a TI model, or with an Enhanced TI (ETI) model that includes a shortwave radiation (SW) scheme and site specific ablation factors. In the ablation zone during other seasons and during all seasons in the accumulation zone, sublimation and other wind-driven ablation processes also contribute to mass loss, and remain unresolved with TI or ETI methods.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".