Policy Options for the World's Primary Forests in Multilateral Environmental Agreements
Bibliographic record
Abstract
Abstract We identify policies that would provide a solid foundation in key international negotiations to ensure that primary forests persist into the 21st Century. A novel compilation of primary forest cover and other data revealed that protection of primary forests is a matter of global concern being equally distributed between developed and developing countries. Almost all (98%) of primary forest is found within 25 countries with around half in five developed ones (USA, Canada, Russia, Australia, and NZ). Only ∼22% of primary forest is found in IUCN Protected Areas Categories I–VI, which is approximately 5% of preagriculture natural forest cover. Rates of deforestation and forest degradation are rapid and extensive, and the long‐term integrity of primary forest cannot be assumed. We recommend four new actions that could be included in climate change, biodiversity, and sustainable development negotiations: (1) recognize primary forests as a matter of global concern within international negotiations; (2) incorporate primary forests into environmental accounting; (3) prioritize the principle of avoided loss; and (4) universally accept the important role of indigenous and community conserved areas. In the absence of specific policies for primary forest protection, their unique biodiversity values and ecosystem services will continue to erode.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".