Supraglacial debris cover assessment in the CaucasusMountains, 1986-2000-2014
Bibliographic record
Abstract
Abstract. Surpaglacial debris cover plays an increasingly important role impacting on glacier ablation, while there have been limited recent studies for the assessment of debris covered glaciers in the Greater Caucasus mountains. We selected 559 glaciers according to the sections and macroslopes in the Greater Caucasus main watershed range and the Elbrus massif to assess supraglacial debris cover (SDC) for the years 1986, 2000 and 2014. Landsat (Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI) and SPOT satellite imagery were analysed to generate glacier outlines using manual and semi-automated methods, along with slope information from a Digital Elevation Model. The study shows there is greater SDC area on the northern than the southern macroslope, and more in the eastern section than the western and central. In 1986-2000-2014, the SDC area increased from 6.4 %-8.2 %-19.4 % on the northern macroslope (apart from the eastern Greater Caucasus section), while on the southern macroslope, SDC increased from 4.0 %-4.9 %-9.2 %. Overall, debris covered glacier numbers increased from 122-143-172 (1986-2000-2014) for 559 selected glaciers. Despite the total glacier area decrease, the SDC glacier area and numbers increased as a function of slope inclination, aspect, glacier morphological type, Little Ice Age (LIA) moraines, rock structure and elevation. The datasets are available for public download at https://doi.pangaea.de/10.1594/PANGAEA.880147.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".