Bolder science needed now for protected areas
Bibliographic record
Abstract
Recognizing that protected areas (PAs) are essential for effective biodiversity conservation action, the Convention on Biological Diversity established ambitious PA targets as part of the 2020 Strategic Plan for Biodiversity. Under the strategic goal to "improve the status of biodiversity by safeguarding ecosystems, species, and genetic diversity," Target 11 aims to put 17% of terrestrial and 10% of marine regions under PA status by 2020. Additionally and crucially, these areas are required to be of particular importance for biodiversity and ecosystem services, effectively and equitably managed, ecologically representative, and well-connected and to include "other effective area-based conservation measures" (OECMs). Whereas the area-based targets are explicit and measurable, the lack of guidance for what constitutes important and representative; effective; and OECMs is affecting how nations are implementing the target. There is a real risk that Target 11 may be achieved in terms of area while failing the overall strategic goal for which it is established because the areas are poorly located, inadequately managed, or based on unjustifiable inclusion of OECMs. We argue that the conservation science community can help establish ecologically sensible PA targets to help prioritize important biodiversity areas and achieve ecological representation; identify clear, comparable performance metrics of ecological effectiveness so progress toward these targets can be assessed; and identify metrics and report on the contribution OECMs make toward the target. By providing ecologically sensible targets and new performance metrics for measuring the effectiveness of both PAs and OECMs, the science community can actively ensure that the achievement of the required area in Target 11 is not simply an end in itself but generates genuine benefits for biodiversity.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.113 | 0.033 |
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".