Managing Protected Areas for Biological Diversity and Ecosystem Functions
Bibliographic record
Abstract
Protected areas are the cornerstones of global efforts to conserve biodiversity.Biological diversity (biodiversity) and ecosystem functions are the fundamental components of any ecosystem (Box 21.1) that protected area managers must consider to be successful.This chapter looks at the relationship between biological diversity and ecological function, the threats to each, and how to assess and monitor ecosystems.Increasingly, protected areas are the last places left for much of the planet's biodiversity.The International Union for Conservation of Nature (IUCN) Red List of Threatened Species that have a high risk of global extinction reveals that many of these species are now found only in protected areas (Le Saout et al. 2013).For example, Javan rhinos (Rhinoceros sondaicus) are found only in Indonesia's Ujung Kulon National Park.Similarly Indian rhinos (Rhinoceros unicornis), once widespread throughout Asia, are now restricted to protected areas, including Kaziranga National Park in India and Royal Chitwan National Park in Nepal.Conserving biodiversity in protected areas means conserving both species and the ecological functions upon which those species depend.
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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 | 0.001 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".