Assessing the extent to which African wetland inventories can report to the global targets on biodiversity, including Goal A of the Global Biodiversity Framework
Bibliographic record
Abstract
Wetlands face global threats with estimates suggesting a loss ranging from 21% to 85% of their original extent. Africa’s wetlands, covering about 4.4% of the continent, provide crucial services to millions of people and harbour significant biodiversity. This study assesses African countries’ readiness for the Kunming−Montreal Global Biodiversity Framework (GBF) through wetland inventories. It examines the progress made under the Aichi Targets for 2020, revealing 39% of reporting African countries indicating being on track for Aichi Target 11. Subsequently, the study explores wetland inventorying trends from national reports made to the Ramsar Convention from COP07 (1999) to COP14 (2022), showcasing increased membership and progress in African countries. Notable patterns emerge, revealing challenges in maintaining accurate inventories. Despite varying responses, 67% of African countries reported having a comprehensive wetland inventory at one point in the past 23 years. However, the wetland inventories are out of date, and critical information is not easily accessible, hampering accurate reporting on the current state of wetlands, and hindering informed decision-making for conservation and protection initiatives. Global datasets have contributed to reporting on wetland ecosystems; however, biodiversity-focused wetland typology systems like the IUCN Global Ecosystem Typology are crucial for a comprehensive understanding of wetland ecosystems.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".