Climate Change and Tropical Andean Glacier Recession: Evaluating Hydrologic Changes and Livelihood Vulnerability in the Cordillera Blanca, Peru
Bibliographic record
Abstract
Climate change is forcing dramatic glacier mass loss in the Cordillera Blanca, Peru, resulting in hydrologic transformations across the Rio Santa watershed and increasing human vulnerability. This article presents results from two years of transdisciplinary collaborative research evaluating the complex relationships between coupled environmental and social change in the region. First, hydrologic results suggest there has been an average increase of 1.6 (± 1.1) percent in the specific discharge of the more glacier-covered catchments (>20 percent glacier area) as a function of changes in stable isotopes of water (δ18O and δ2H) from 2004 to 2006. Second, there is a large (mean 60 percent) component of groundwater in dry season discharge based on results from the hydrochemical basin characterization method. Third, findings from extensive key interviews and seventy-two randomly sampled household interviews within communities located in two case study watersheds demonstrate that a large majority of households perceive that glacier recession is proceeding very rapidly and that climate change–related impacts are affecting human vulnerability across multiple shifting vectors including access to water resources, agro-pastoral production, and weather variability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".