Relating glacier mass balance to meteorological data by using a seasonal sensitivity characteristic
Bibliographic record
Abstract
Abstract We propose to quantify the climate sensitivity of the mean specific balance B of a glacier by a seasonal sensitivity characteristic (SSC). The SSC gives the dependence of B on monthly anomalies in temperature and precipitation. It is calculated from a mass-balance model. We show and discuss examples for Franz-Josef Glacier (New Zealand), Nigardsbreen (Norway), Hintereisferner (Austria), Peyto Glacier (Canadian Rockies), Abramov Glacier (Kirghizstan) and White Glacier (Canadian Arctic). With regard to the climate sensitivity of B, the SSCs clearly show that summer temperature is the most important factor for glaciers in a dry climate. For glaciers in a wetter climate, spring and fall temperatures also make a significant contribution to the overall sensitivity. The SSC is a 2 × 12 matrix. Multiplying it with monthly perturbations of temperature and precipitation for a particular year yields an estimate of the balance for that year. We show that, with this technique, mass-balance series can be (re)constructed from long meteorological records or from output of atmospheric models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".