Diatoms and other siliceous indicators track the ontogeny of a “bofedal” (wetland) ecosystem in the Peruvian Andes
Bibliographic record
Abstract
Recent warming in the Andes is affecting the region’s water resources including glaciers and lakes, which supply water to tens of millions of people downstream. High-elevation wetlands, known locally as “bofedales”, are an understudied Andean ecosystem despite their key role in carbon sequestration, maintenance of biodiversity, and regulation of water flow. Here, we analyze subfossil diatom assemblages and other siliceous bioindicators preserved in a peat core collected from a bofedal in Peru’s Cordillera Vilcanota. Basal radiocarbon ages show the bofedal likely formed during a wet period of the Little Ice Age (1520–1680 CE), as inferred from nearby ice core data. The subfossil diatom record is marked by several dynamic assemblage shifts documenting a hydrosere succession from an open-water system to mature peatland. The diatoms appear to be responding largely to changes in hydrology that occur within the natural development of the bofedal, but also to pH and possibly nutrient enrichment from grazing animals. The rapid peat accretion recorded post-1950 at this site is consistent with recent peat growth rates elsewhere in the Andes. Given the many threats to Peruvian bofedales including climate change, overgrazing, peat extraction, and mining, these baseline data will be critical to assessing future change in these important ecosystems.
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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 | 0.001 | 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".