Spatial and Temporal Transferability of a Distributed Energy-Balance Glacier Melt Model
Bibliographic record
Abstract
Abstract Modeling melt from glaciers is crucial to assessing regional hydrology and eustatic sea level rise. The transferability of such models in space and time has been widely assumed but rarely tested. To investigate melt model transferability, a distributed energy-balance melt model (DEBM) is applied to two small glaciers of opposing aspects that are 10 km apart in the Donjek Range of the St. Elias Mountains, Yukon Territory, Canada. An analysis is conducted in four stages to assess the transferability of the DEBM in space and time: 1) locally derived model parameter values and meteorological forcing variables are used to assess model skill; 2) model parameter values are transferred between glacier sites and between years of study; 3) measured meteorological forcing variables are transferred between glaciers using locally derived parameter values; 4) both model parameter values and measured meteorological forcing variables are transferred from one glacier site to the other, treating the second glacier site as an extension of the first. The model parameters are transferable in time to within a <10% uncertainty in the calculated surface ablation over most or all of a melt season. Transferring model parameters or meteorological forcing variables in space creates large errors in modeled ablation. If select quantities (ice albedo, initial snow depth, and summer snowfall) are retained at their locally measured values, model transferability can be improved to achieve ≤15% uncertainty in the calculated surface ablation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".