Detection of changes in glacial run‐off in alpine basins: examples from North America, the Alps, central Asia and the Andes
Bibliographic record
Abstract
Abstract Atmospheric warming and enhanced melting of glaciers is already resulting in changes in the glacial contribution to run‐off in mountain basins around the world. The enhanced melting of glaciers leads at first to increased run‐off and discharge peaks and an increased melt season, while in the longer time frame glacier wasting can be so severe that it results in decreased run‐off. Glacier basins with a decreasing run‐off trend have been observed in south‐central British Columbia, at low elevations in the Swiss Alps and in the central Andes of Chile, which is probably a combined effect of reduced melt from seasonal snow cover as the snow line rises, and relevant glacier area losses. In contrast, significant run‐off increases are reported in Alberta, north‐western British Columbia and Yukon in Canada, in highly glacierized basins in the Swiss and Austrian Alps, the Tianshan Mountains and Tibet in central Asia and in the tropical Andes of Peru. The run‐off increase within these basins is closely related to observed temperature rise, indicating that there is an unequivocal signal of enhanced glacier melting under the present warming trends. In future warming scenarios, glacier run‐off should start to decrease even in high‐altitude basins, affecting water availability. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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".