Peatlands of the Peruvian Puna Ecoregion: Types, Characteristics and Disturbance
Bibliographic record
Abstract
Peatlands represent one of the most important water resources in the Puna grassland ecoregion, but this fact is not yet widely recognised. Puna peatlands also provide key environmental services such as increasing the regional biodiversity of the Andean Altiplano plateau and contributing to the wellbeing of high-altitude human populations by providing grazing land and cooking fuel. We conducted a study in the Peruvian Puna ecoregion to describe the current condition of peatlands in terms of their vegetation, physical and chemical characteristics and disturbance status. Our results suggest that peat thickness, organic matter and degree of humification are good indicators for identifying peatlands in the Puna ecoregion. In general, the peatland sites that we sampled were dominated by mixtures of cushion and acaulescent rosette forming plants such as Distichia muscoides Nees & Meyen and Plantago tubulosa Decne. These Distichia and Plantago peatland sites were characterised by a mean surface water pH of 6.3, corrected electrical conductivity (K corr.) in the range 300–1814 μS cm-1 and presented the following mean exchangeable cation values: Ca2+ 48 mg L-1, Mg2+ 9.6 mg L-1, Na+ 8.2 mg L-1 and K+ 2.1 mg L-1. The most common causes of disturbance we encountered were grazing, peat extraction and roads. Disturbance was most severe in mining sites, where peatlands are especially vulnerable because they are not under legal protection.
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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 | 0.000 |
| 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".