Achieving Aichi Biodiversity Target 11 to improve the performance of protected areas and conserve freshwater biodiversity
Bibliographic record
Abstract
Abstract The Strategic Plan for Biodiversity (2011–2020), adopted at the 10th meeting of the Conference of the Parties to the Convention on Biological Diversity, sets 20 Aichi Biodiversity Targets to be met by 2020 to address biodiversity loss and ensure its sustainable and equitable use. Aichi Biodiversity Target 11 describes what an improved conservation network would look like for marine, terrestrial and inland water areas, including freshwater ecosystems. To date, there is no comprehensive assessment of what needs to be achieved to meet Target 11 for freshwater biodiversity. Reports on implementation often fail to consider explicitly freshwater ecosystem processes and habitats, the pressures upon them, and therefore the full range of requirements and actions needed to sustain them. Here the current progress and key gaps for meeting Aichi Target 11 are assessed by exploring the implications of each of its clauses for freshwater biodiversity. Concerted action on Aichi Biodiversity Target 11 for freshwater biodiversity by 2020 is required in a number of areas: a robust baseline is needed for each of the clauses described here at national and global scales; designation of new protected areas or expansion of existing protected areas to cover known areas of importance for biodiversity and ecosystem services, and a representative sample of biodiversity; use of Other Effective Area‐Based Conservation Measures (OECMs) in places where designating a protected area is not appropriate; and promoting and implementing better management strategies for fresh water in protected areas that consider its inherent connectivity, contextual vulnerability, and required human and technical capacity. Considering the specific requirements of freshwater systems through Aichi Biodiversity Target 11 has long‐term value to the Sustainable Development Goals discussions and global conservation policy agenda into the coming decades. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".