Characteristics of Surface Dust on Ürümqi Glacier No. 1 in the Tien Shan Mountains, China
Bibliographic record
Abstract
Monitoring studies show that many mountain glaciers worldwide are decreasing in mass. An important component of the process of ice mass loss is the effect of dust on albedo and its effect on glacier mass balance. The characteristics of surface dust were investigated in August 2006 on the Ürümqi Glacier No. 1 in the Tien Shan Mountains, China. The bare ice surface of the glacier was mostly covered by brown dust. The amounts of surface dust on the ice surface (dry weight) ranged from 86 to 1113 g m−2 (mean: 335 g m−2, standard deviation: = 211), which is within the normal range for Asian glaciers, but significantly greater than those on glaciers in other regions such as Alaska, Patagonia, and the Canadian Arctic. An analysis of organic matter and microscopy of the surface dust revealed that the dust contained high levels of organic matter, including living cyanobacteria. This suggests that it is comprised not only of deposits of wind-blown desert dust, but is also a product of microbial activity on the glacier itself. Spectral albedo of the glacial surface showed spectrum curves typical of those of snow and ice contaminated with dust. The integrated surface albedo ranged from 0.09 to 0.24 (mean: 0.14) in the ice area, from 0.50 to 0.64 (mean: 0.56) in the snow area. The lower albedo on the glacial surface compared with that of clean bare ice or snow surface suggests that the albedo was significantly reduced by the surface dust on this glacier. Results suggest that the mineral and organic dust on the glacial surface substantially accounts for the recent shrinkage of the glacier.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".