Arguments for protected areas: multiple benefits for conservation and use
Bibliographic record
Abstract
Foreword: Arguments for Protected Areas 1. Protected Areas: Linking Environment and Well-Being 2. Vital Sites: Protected Areas Supporting Health and Recreation Case study 2.1: Protecting Medicinal Resources in Colombia Case Study 2.2: Parks Victoria (Australia) 'Healthy Parks Healthy People Initiative' 3. Running Pure: Protected Areas Maintaining Purity and Quantity of Urban Water Supplies Case Study 3.1: Protecting Water Supplies to Caracas, Venezuela 4. Food Stores: Protected Areas Conserving Crop Wild Relatives and Securing Future Food Stocks Case Study 4.1: Crop Genetic Diversity Protection in Turkey Case Study 4.2: Conservation of Endangered CWRs in Mexico s Sierra de Manantl n 5. Nursery Tails: Protected Areas Conserving Wild Marine and Freshwater Fish Stocks Case Study 5.1: Freshwater Fishery Sustainability in Lake Malawi, East Africa Case Study 5.2: Managing and Monitoring Success: The Story of Tubbataha Reefs, Philippines 6. Natural Security: Protected Areas and Hazard Mitigation Case Study 6.1: Environmental Degradation and The Indian Ocean Tsunami of 2004 Case Study 6.2: Restoration and Protection Plan to Reduce Flooding in the Lower Danube 7. Safety Net: Protected Areas Contributing to Human Well-Being Case Study 7.1: Population-Health-Environment Approaches in Kiunga Marine National Reserve, Kenya 8. Beyond Belief: Linking Faiths and Protected Areas to Support Biodiversity Conservation Case Study 8.1: The Ancient Sacred Natural Sites in Al Hoceima National Park, Morocco 9. Living Traditions: Protected Areas and Cultural Diversity Case Study 9.1: Angkor Wat Protected Landscape: Where Culture, Nature and Spirit Meet Case Study 9.2: Inuit Partnerships in the Torngat Mountains National Park, Canada 10. Diverting Places: Linking Travel, Pleasure and Protection Case Study 10.1: Managing Tourism in South Korea s Protected Area System 11. Climate Change: The Role of Protected Areas in Mitigating and Adapting to Change Case Study 11.1: Protected Areas Helping to Reduce Carbon Emissions in Brazil Britaldo Silveira 12. Making Peace: Protected Areas Contributing to Conflict Resolution Case Study 12.1: La Amistad Binational Biosphere Reserve in Costa Rica and Panama 13. Nature Conservation: Leaving Space for Biodiversity Case Study 13.1: One Hundred Years of Conservation Success at Kaziranga National Park, India 14. Precious Places: Getting the Arguments Right Index
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".