The Greater Caucasus Glacier Inventory (Russia, Georgia and Azerbaijan)
Bibliographic record
Abstract
Abstract. While there are a large number of glaciers in the Greater Caucasus, the region is not fully represented in modern glacier databases with previous incomplete inventories. Here, we present an expanded glacier inventory for this region over the 1960–1986–2014 period. Large scale topographic maps and satellite imagery (Landsat 5, Landsat 8 and ASTER) were used to conduct a remote sensing survey of glacier change in the Greater Caucasus mountains. Glacier margins were mapped manually and reveal that, in 1960, the mountains contained 2349 glaciers, with a total glacier surface area of 1674.9 ± 35.2 km2. By 1986, glacier surface area had decreased to 1482.1 ± 32.2 km2 (2209 glaciers), and by 2014, to 1193.2 ± 27.0 km2 (2020 glaciers). This represents a 28.8 ± 2.2 % (481 ± 10.6 km2) reduction in total glacier surface area between 1960 and 2014 and a marked acceleration in the rate of area loss since 1986. Analysis of possible controls suggest that the general decreases in both glacier area and number for the period 1960–2014 are directly due to general increase in temperature, especially in summer (June–July–August), although the response of individual glaciers was modulated by other factors, including glacier size, elevation, rock structure, exposition, morphological type and debris cover. This new glacier inventory can be used as a basis dataset for future studies including glacier change assessment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.003 | 0.001 |
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".