Supra-glacial debris cover changes in the Greater Caucasus from 1986 to 2014
Bibliographic record
Abstract
Abstract. Knowledge of supra-glacial debris cover and its changes remain incomplete in the Greater Caucasus, in spite of recent glacier studies. Here we present data of supra-glacial debris cover for 659 glaciers across the Greater Caucasus based on Landsat and SPOT images from the years 1986, 2000 and 2014. We combined semi-automated methods for mapping the clean ice with manual digitization of debris-covered glacier parts and calculated supra-glacial debris-covered area as the residual between these two maps. The accuracy of the results was assessed by using high-resolution Google Earth imagery and GPS data for selected glaciers. From 1986 to 2014, the total glacier area decreased from 691.5±29.0 to 590.0±25.8 km2 (15.8±4.1 %, or ∼0.52 % yr−1), while the clean-ice area reduced from 643.2±25.9 to 511.0±20.9 km2 (20.1±4.0 %, or ∼0.73 % yr−1). In contrast supra-glacial debris cover increased from 7.0±6.4 %, or 48.3±3.1 km2, in 1986 to 13.4±6.2 % (∼0.22 % yr−1), or 79.0±4.9 km2, in 2014. Debris-free glaciers exhibited higher area and length reductions than debris-covered glaciers. The distribution of the supra-glacial debris cover differs between the northern and southern and between the western, central and eastern Greater Caucasus. The observed increase in supra-glacial debris cover is significantly stronger on the northern slopes. Overall, we have observed up-glacier average migration of supra-glacial debris cover from about 3015 to 3130 m a.s.l. (metres above sea level) during the investigated period.
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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 | 0.000 | 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) | 0.012 | 0.002 |
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; both teacher heads agree on what is shown here.
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".