Glaciological conditions in seven contrasting regions estimated with the degree-day model
Bibliographic record
Abstract
Abstract We apply the degree-day model to seven glacial regions that offer contrasting conditions and are well documented in the World Glacier Inventory. The regions are: Axel Heiberg Island in Arctic Canada; Svalbard; northern Scandinavia; southern Norway; the Alps; the Caucasus; and New Zealand. We estimate the average equilibrium-line altitude (ELA) for each half-degree latitude/longitude grid square from the median elevations of glaciers within the square and we extrapolate temperature from the UEA/CRU (Climate Research Unit of the University of East Anglia) gridded climatology. Using the degree-day model, we calculate annual accumulation at the ELA, equal to ablation at the ELA, and other quantities like summer mean temperature, length of melt season, balance gradients and the sensitivity of mass balance to temperature and/or precipitation changes. Glaciers can be characterized on a scale from cold-dry (Axel Heiberg Island) to warm-wet (New Zealand) corresponding to the contrast between maritime and continental climates. Mass-balance sensitivities to temperature and/or precipitation changes are relatively small for dry-cold climate and relatively high for warm-wet climate. We could extend the approach to other glacier regions but we note that there are large areas for which ELA data are not available as they are still not covered by the World Glacier Inventory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".