Indonesia's protected areas need more protection: suggestions from island examples
Bibliographic record
Abstract
Introduction Intact, biodiverse ecosystems provide invaluable life-support services, raw natural resources, and cultural necessities ranging from recreational to spiritual. Moreover, they are literally economically priceless (Costanza et al . 1997). It is widely appreciated that ‘biodiversity is good’ and that ultimately, human well-being and persistence will depend on our ability to preserve it for future generations. Biodiverse ecosystems, however, are not evenly distributed on our planet – they are patchy and concentrated in tropical regions (Myers et al . 2000). Likewise, costs and benefits of conserving biodiversity are not evenly distributed (Balmford et al . 2003). Our ability to conserve biological diversity is constrained by global trends of exploitation, pollution and habitat loss – all increasing because of human-population growth. Unfortunately, areas of accelerating human population growth overlap many areas of highest biodiversity where resources to protect this diversity are fewest (Cincotta et al . 2000) and land-conversion pressures greatest. As human populations continue to expand, we are faced with even more pressing needs to conserve and protect diverse ecosystems. Protected areas: theory meets reality Protected areas are, by definition, designed to protect biological diversity from threats to its continued existence. They are the cornerstone of most biodiversity efforts because species need habitats and they might be the best way to ensure the long-term conservation of biodiversity (du Toit et al . 2004). Unfortunately, many protected areas are only ‘paper parks’ that are not only highly degraded, but also the target of continuing exploitation (Curran et al . 2004).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".