Glacier recession and human vulnerability in the Yanamarey watershed of the Cordillera Blanca, Peru
Bibliographic record
Abstract
Glaciers in the Cordillera Blanca, Peru, are undergoing rapid retreat, in large part due to climate change. These changes are significantly altering water availability in the region and pose critical risks to local populations that are highly dependent on these resources for livelihoods. We examine these issues through an interdisciplinary and linked evaluation of hydrological change and livelihood vulnerability in the Yanamarey watershed. Physical observations of the Yanamarey glacier show acceleration in frontal retreat at a rate of 8 m decade − 1 since 1970, accompanied by total volume loss on the order of 0.022 km 3 . Hydrological and hydrochemical analyses document a possible transformation of stream flow over the past decade as the seasonal storage capacity of the glacier has degraded. Recent stream discharge measurements from the proglacial lake below the glacier are more coincident with the highly variable seasonal precipitation than they were during the 1998–1999 hydrological year. Local household perceptions of glacier recession and seasonal hydrological variability agree with this trend, which is increasing human vulnerability in the watershed. Household case-study survey results demonstrate that shifting water resources, increasing weather extremes and climate-related threats to tourism are all new vectors of vulnerability for household livelihoods.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".