Identification, Mapping and Ethnobotany of Plant Species in the Peruvian High Andean Wetlands: Stimulating Biodiversity Conservation Efforts towards Sustainability
Bibliographic record
Abstract
The high Andean wetlands of Peru provide not only the well-known ecosystem services such as water storage, flood mitigation, erosion control, and purification of water, but are also a source of income for local economies (as fodder), have medicinal properties, are a source of food, contribute to the development of ecotourism among many other uses. Economic and population growth have already damaged many parts of the high Andean wetlands including their rich flora. In order to promote the conservation of its diversity and unique flora, this study conducted extensive botanical explorations to identify and map the floristic composition of the high Andean wetlands of La Libertad, Peru, as well as their influence on local communities. The authors conducted explorations taking taxonomic, biogeographic and ethno biological data of the flora species as well as their therapeutic and economic botany. The study identified 64 species of flora distributed in 46 genera and 27 families including Asteraceae (with 8 species), Juncaceae (with 7 species), Poaceae (with 6 species), Cyperaceae (with 5 species), Licopodiaceae and Rosaceae (with 4 species each), Apiaceae, Gentianaceae, Orobanchaceae and Sphagnaceae (with 3 species each) and Poligonaceae (with 2 species). With reference to economic botany, it was found that 32.8% of species constitute resources with a very good economic benefit. The study concludes that it is imperative to take actions to protect the high Andean wetlands as they are ecosystems with great biodiversity. This study contribution expects to raise concerns regarding the increasing impact of economic and population growth on the loss of not only natural habitats but species as well. Conservation efforts will help protect the heritage of the Andes wetlands for future generations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".