The Greater Caucasus Glacier Inventory (Russia, Georgia and Azerbaijan)
Bibliographic record
Abstract
Abstract. There have been numerous studies of glaciers in the Greater Caucasus, but none that have generated a modern glacier database across the whole mountain range. Here, we present an updated and expanded glacier inventory at three time periods (1960, 1986, 2014) covering the entire Greater Caucasus. Large-scale topographic maps and satellite imagery (Corona, Landsat 5, Landsat 8 and ASTER) were used to conduct a remote-sensing survey of glacier change, and the 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM; 17 November 2011) was used to determine the aspect, slope and height distribution of glaciers. Glacier margins were mapped manually and reveal that in 1960 the mountains contained 2349 glaciers with a total glacier surface area of 1674.9 ± 70.4 km2. By 1986, glacier surface area had decreased to 1482.1 ± 64.4 km2 (2209 glaciers), and by 2014 to 1193.2 ± 54.0 km2 (2020 glaciers). This represents a 28.8 ± 4.4 % (481 ± 21.2 km2) or 0.53 % yr−1 reduction in total glacier surface area between 1960 and 2014 and an increase in the rate of area loss since 1986 (0.69 % yr−1) compared to 1960–1986 (0.44 % yr−1). Glacier mean size decreased from 0.70 km2 in 1960 to 0.66 km2 in 1986 and to 0.57 km2 in 2014. This new glacier inventory has been submitted to the Global Land Ice Measurements from Space (GLIMS) database and can be used as a basis data set for future studies.
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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".