Evaluating the transferability of empirical models of debris-covered glacier melt
Bibliographic record
Abstract
Supraglacial debris is significant in many regions and complicates modeling of glacier melt, which is required for predicting glacier change and its influences on hydrology and sea-level rise. Temperature-index models are a popular alternative to energy-balance models when forcing data are limited, but their transferability among glaciers and inherent uncertainty have not been documented in application to debris-covered glaciers. Here, melt factors were compiled directly from published studies or computed from reported melt and MERRA-2 air temperature for 27 debris-covered glaciers around the world. Linear mixed-effects models were fit to predict melt factors from debris thickness and variables including debris lithology and MERRA-2 radiative exchange. The models were tested by leave-one-site-out cross-validation based on predicted melt rates. The best model included debris thickness (fixed effect) and glacier and year (random effects). Predictions were more accurate using MERRA-2 than on-site air temperature data, and pooling MERRA-2-derived and reported melt factors improved cross-validation accuracy more than including additional predictors such as shortwave or longwave radiation. At one glacier where monthly ablation was measured over 4 years, seasonal variation of melt factors suggested that heat storage significantly affected the relation between melt and energy exchange at the debris surface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".